feat: NCS-53 changed IO to MicroService for easier scaling (#37 )

Reviewed-on: #37 Reviewed-by: Shahd Lala <shosho996@blackhole.local>
ci: bump version with Build-44
2026-04-21 15:38:58 +02:00 · 2026-04-21 11:13:30 +00:00 · 2026-04-21 13:02:18 +02:00 · 2026-04-21 12:39:19 +02:00 · 2026-04-21 12:35:20 +02:00 · 2026-04-19 20:53:56 +00:00
239 changed files with 12287 additions and 2117 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.**

 ---

@@ -47,19 +47,130 @@
  - class Square
  - function fromAlgebraic
  - function offset
+- `modules/api/src/main/scala/de/nowchess/api/bot/Bot.scala`
+  - class Bot
+  - function name
+  - function nextMove
+- `modules/api/src/main/scala/de/nowchess/api/dto/ErrorEventDto.scala` — class ErrorEventDto, function apply
+- `modules/api/src/main/scala/de/nowchess/api/dto/GameFullEventDto.scala` — class GameFullEventDto, function apply
+- `modules/api/src/main/scala/de/nowchess/api/dto/GameStateEventDto.scala` — class GameStateEventDto, function apply
 - `modules/api/src/main/scala/de/nowchess/api/game/GameContext.scala`
+  - function kingSquare
  - function withBoard
  - function withTurn
  - function withCastlingRights
  - function withEnPassantSquare
  - function withHalfMoveClock
-  - function withMove
-  - _...2 more_
+  - _...4 more_
 - `modules/api/src/main/scala/de/nowchess/api/player/PlayerInfo.scala` — class PlayerId, function apply
 - `modules/api/src/main/scala/de/nowchess/api/response/ApiResponse.scala`
  - 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/lichess_importer.py` — function import_lichess_evals: (input_path, output_file, max_positions, min_depth, verbose) -> int
+- `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, hidden_sizes)
+  - 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, hidden_sizes)
+  - class NNUEDataset
+  - _...1 more_
+- `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
+- `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 advance
+  - 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
@@ -74,15 +185,16 @@
  - function history
  - function getCurrentIndex
  - _...3 more_
+- `modules/core/src/main/scala/de/nowchess/chess/config/JacksonConfig.scala` — class JacksonConfig, function customize
 - `modules/core/src/main/scala/de/nowchess/chess/controller/Parser.scala` — class Parser, function parseMove
 - `modules/core/src/main/scala/de/nowchess/chess/engine/GameEngine.scala`
  - class GameEngine
-  - function isPendingPromotion
  - function board
  - function turn
  - function context
  - function canUndo
-  - _...10 more_
+  - function canRedo
+  - _...11 more_
 - `modules/core/src/main/scala/de/nowchess/chess/observer/Observer.scala`
  - function context
  - class Observer
@@ -91,8 +203,35 @@
  - function subscribe
  - function unsubscribe
  - _...1 more_
+- `modules/core/src/main/scala/de/nowchess/chess/registry/GameRegistry.scala`
+  - class GameRegistry
+  - function store
+  - function get
+  - function update
+  - function generateId
+- `modules/core/src/main/scala/de/nowchess/chess/registry/GameRegistryImpl.scala`
+  - class GameRegistryImpl
+  - function store
+  - function get
+  - function update
+  - function generateId
+- `modules/core/src/main/scala/de/nowchess/chess/resource/GameResource.scala`
+  - function onGameEvent
+  - function createGame
+  - function getGame
+  - function streamGame
+  - function onGameEvent
+  - function resignGame
+  - _...9 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 +253,8 @@
  - 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
@@ -131,32 +272,28 @@
  - function allLegalMoves
  - function isCheck
  - function isCheckmate
-  - _...4 more_
+  - _...5 more_
 - `modules/rule/src/main/scala/de/nowchess/rules/sets/DefaultRules.scala`
  - class DefaultRules
+  - function positionOf
  - function loop
  - function toMoves
  - function loop
- `modules/ui/src/main/scala/de/nowchess/ui/Main.scala` — class Main, function main
- `modules/ui/src/main/scala/de/nowchess/ui/gui/ChessBoardView.scala`
-  - class ChessBoardView
-  - function updateBoard
-  - function updateUndoRedoButtons
-  - function showMessage
-  - function showPromotionDialog
- `modules/ui/src/main/scala/de/nowchess/ui/gui/ChessGUI.scala`
-  - class ChessGUIApp
-  - class ChessGUILauncher
-  - function getEngine
-  - function launch
- `modules/ui/src/main/scala/de/nowchess/ui/gui/GUIObserver.scala` — class GUIObserver
- `modules/ui/src/main/scala/de/nowchess/ui/gui/PieceSprites.scala`
-  - class PieceSprites
-  - function loadPieceImage
-  - class SquareColors
- `modules/ui/src/main/scala/de/nowchess/ui/terminal/TerminalUI.scala` — class TerminalUI, function start
- `modules/ui/src/main/scala/de/nowchess/ui/utils/PieceUnicode.scala` — function unicode
- `modules/ui/src/main/scala/de/nowchess/ui/utils/Renderer.scala` — class Renderer, function render
+
+---
+
+# Config
+
+## Environment Variables
+
+- `STOCKFISH_PATH` **required** — modules/bot/python/nnue.py
+
+---
+
+# Middleware
+
+## custom
+- generate — `modules/bot/python/src/generate.py`

 ---

@@ -164,39 +301,39 @@

 ## 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/api/src/main/scala/de/nowchess/api/game/GameContext.scala` — imported by **64** files
+- `modules/api/src/main/scala/de/nowchess/api/move/Move.scala` — imported by **44** files
+- `modules/api/src/main/scala/de/nowchess/api/board/Square.scala` — imported by **40** files
+- `modules/api/src/main/scala/de/nowchess/api/board/Color.scala` — imported by **35** files
+- `modules/api/src/main/scala/de/nowchess/api/board/Board.scala` — imported by **19** files
+- `modules/api/src/main/scala/de/nowchess/api/board/Piece.scala` — imported by **18** files
+- `modules/api/src/main/scala/de/nowchess/api/board/PieceType.scala` — imported by **17** 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 **11** 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 **9** files
+- `modules/api/src/main/scala/de/nowchess/api/game/DrawReason.scala` — imported by **7** 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/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
+- `modules/api/src/main/scala/de/nowchess/api/bot/Bot.scala` — imported by **6** files
+- `modules/bot/src/main/scala/de/nowchess/bot/ai/Evaluation.scala` — imported by **6** files
+- `modules/api/src/main/scala/de/nowchess/api/player/PlayerInfo.scala` — imported by **5** files
+- `modules/bot/src/main/scala/de/nowchess/bot/util/PolyglotBook.scala` — imported by **5** files
+- `modules/api/src/main/scala/de/nowchess/api/game/GameResult.scala` — imported by **4** 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

 ## 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/api/src/main/scala/de/nowchess/api/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` +59 more
+- `modules/api/src/main/scala/de/nowchess/api/move/Move.scala` ← `modules/api/src/main/scala/de/nowchess/api/bot/Bot.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/BotMoveRepetition.scala` +39 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` +35 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/main/scala/de/nowchess/api/game/GameResult.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` +30 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` +14 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` +13 more
+- `modules/api/src/main/scala/de/nowchess/api/board/PieceType.scala` ← `modules/api/src/main/scala/de/nowchess/api/game/GameContext.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` +12 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` +6 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/main/scala/de/nowchess/chess/resource/GameResource.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` +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/api/src/main/scala/de/nowchess/api/game/GameContext.scala` — imported by **64** files
+- `modules/api/src/main/scala/de/nowchess/api/move/Move.scala` — imported by **44** files
+- `modules/api/src/main/scala/de/nowchess/api/board/Square.scala` — imported by **40** files
+- `modules/api/src/main/scala/de/nowchess/api/board/Color.scala` — imported by **35** files
+- `modules/api/src/main/scala/de/nowchess/api/board/Board.scala` — imported by **19** files
+- `modules/api/src/main/scala/de/nowchess/api/board/Piece.scala` — imported by **18** files
+- `modules/api/src/main/scala/de/nowchess/api/board/PieceType.scala` — imported by **17** 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 **11** 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 **9** files
+- `modules/api/src/main/scala/de/nowchess/api/game/DrawReason.scala` — imported by **7** 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/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
+- `modules/api/src/main/scala/de/nowchess/api/bot/Bot.scala` — imported by **6** files
+- `modules/bot/src/main/scala/de/nowchess/bot/ai/Evaluation.scala` — imported by **6** files
+- `modules/api/src/main/scala/de/nowchess/api/player/PlayerInfo.scala` — imported by **5** files
+- `modules/bot/src/main/scala/de/nowchess/bot/util/PolyglotBook.scala` — imported by **5** files
+- `modules/api/src/main/scala/de/nowchess/api/game/GameResult.scala` — imported by **4** 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

 ## 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/api/src/main/scala/de/nowchess/api/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` +59 more
+- `modules/api/src/main/scala/de/nowchess/api/move/Move.scala` ← `modules/api/src/main/scala/de/nowchess/api/bot/Bot.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/BotMoveRepetition.scala` +39 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` +35 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/main/scala/de/nowchess/api/game/GameResult.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` +30 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` +14 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` +13 more
+- `modules/api/src/main/scala/de/nowchess/api/board/PieceType.scala` ← `modules/api/src/main/scala/de/nowchess/api/game/GameContext.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` +12 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` +6 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/main/scala/de/nowchess/chess/resource/GameResource.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` +5 more
@@ -38,19 +38,130 @@
  - class Square
  - function fromAlgebraic
  - function offset
+- `modules/api/src/main/scala/de/nowchess/api/bot/Bot.scala`
+  - class Bot
+  - function name
+  - function nextMove
+- `modules/api/src/main/scala/de/nowchess/api/dto/ErrorEventDto.scala` — class ErrorEventDto, function apply
+- `modules/api/src/main/scala/de/nowchess/api/dto/GameFullEventDto.scala` — class GameFullEventDto, function apply
+- `modules/api/src/main/scala/de/nowchess/api/dto/GameStateEventDto.scala` — class GameStateEventDto, function apply
 - `modules/api/src/main/scala/de/nowchess/api/game/GameContext.scala`
+  - function kingSquare
  - function withBoard
  - function withTurn
  - function withCastlingRights
  - function withEnPassantSquare
  - function withHalfMoveClock
-  - function withMove
-  - _...2 more_
+  - _...4 more_
 - `modules/api/src/main/scala/de/nowchess/api/player/PlayerInfo.scala` — class PlayerId, function apply
 - `modules/api/src/main/scala/de/nowchess/api/response/ApiResponse.scala`
  - 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/lichess_importer.py` — function import_lichess_evals: (input_path, output_file, max_positions, min_depth, verbose) -> int
+- `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, hidden_sizes)
+  - 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, hidden_sizes)
+  - class NNUEDataset
+  - _...1 more_
+- `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
+- `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 advance
+  - 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
@@ -65,15 +176,16 @@
  - function history
  - function getCurrentIndex
  - _...3 more_
+- `modules/core/src/main/scala/de/nowchess/chess/config/JacksonConfig.scala` — class JacksonConfig, function customize
 - `modules/core/src/main/scala/de/nowchess/chess/controller/Parser.scala` — class Parser, function parseMove
 - `modules/core/src/main/scala/de/nowchess/chess/engine/GameEngine.scala`
  - class GameEngine
-  - function isPendingPromotion
  - function board
  - function turn
  - function context
  - function canUndo
-  - _...10 more_
+  - function canRedo
+  - _...11 more_
 - `modules/core/src/main/scala/de/nowchess/chess/observer/Observer.scala`
  - function context
  - class Observer
@@ -82,8 +194,35 @@
  - function subscribe
  - function unsubscribe
  - _...1 more_
+- `modules/core/src/main/scala/de/nowchess/chess/registry/GameRegistry.scala`
+  - class GameRegistry
+  - function store
+  - function get
+  - function update
+  - function generateId
+- `modules/core/src/main/scala/de/nowchess/chess/registry/GameRegistryImpl.scala`
+  - class GameRegistryImpl
+  - function store
+  - function get
+  - function update
+  - function generateId
+- `modules/core/src/main/scala/de/nowchess/chess/resource/GameResource.scala`
+  - function onGameEvent
+  - function createGame
+  - function getGame
+  - function streamGame
+  - function onGameEvent
+  - function resignGame
+  - _...9 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 +244,8 @@
  - 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
@@ -122,29 +263,10 @@
  - function allLegalMoves
  - function isCheck
  - function isCheckmate
-  - _...4 more_
+  - _...5 more_
 - `modules/rule/src/main/scala/de/nowchess/rules/sets/DefaultRules.scala`
  - class DefaultRules
+  - function positionOf
  - function loop
  - function toMoves
  - function loop
- `modules/ui/src/main/scala/de/nowchess/ui/Main.scala` — class Main, function main
- `modules/ui/src/main/scala/de/nowchess/ui/gui/ChessBoardView.scala`
-  - class ChessBoardView
-  - function updateBoard
-  - function updateUndoRedoButtons
-  - function showMessage
-  - function showPromotionDialog
- `modules/ui/src/main/scala/de/nowchess/ui/gui/ChessGUI.scala`
-  - class ChessGUIApp
-  - class ChessGUILauncher
-  - function getEngine
-  - function launch
- `modules/ui/src/main/scala/de/nowchess/ui/gui/GUIObserver.scala` — class GUIObserver
- `modules/ui/src/main/scala/de/nowchess/ui/gui/PieceSprites.scala`
-  - class PieceSprites
-  - function loadPieceImage
-  - class SquareColors
- `modules/ui/src/main/scala/de/nowchess/ui/terminal/TerminalUI.scala` — class TerminalUI, function start
- `modules/ui/src/main/scala/de/nowchess/ui/utils/PieceUnicode.scala` — function unicode
- `modules/ui/src/main/scala/de/nowchess/ui/utils/Renderer.scala` — class Renderer, function render
@@ -0,0 +1,4 @@
+# Middleware
+
+## custom
+- generate — `modules/bot/python/src/generate.py`
@@ -0,0 +1,198 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+<meta charset="UTF-8">
+<meta name="viewport" content="width=device-width, initial-scale=1.0">
+<title>NowChessSystems — codesight report</title>
+<style>
+*{margin:0;padding:0;box-sizing:border-box}
+:root{--bg:#0a0a0f;--card:#12121a;--border:#1e1e2e;--text:#e0e0e8;--muted:#6b6b80;--accent:#6366f1;--accent2:#22d3ee;--green:#22c55e;--orange:#f59e0b;--red:#ef4444;--pink:#ec4899}
+body{background:var(--bg);color:var(--text);font-family:-apple-system,BlinkMacSystemFont,'Segoe UI',Roboto,sans-serif;padding:2rem;max-width:1400px;margin:0 auto;line-height:1.6}
+h1{font-size:2.5rem;font-weight:800;background:linear-gradient(135deg,var(--accent),var(--accent2));-webkit-background-clip:text;-webkit-text-fill-color:transparent;margin-bottom:.25rem}
+.subtitle{color:var(--muted);font-size:1rem;margin-bottom:2rem}
+.stack-badge{display:inline-block;background:var(--card);border:1px solid var(--border);border-radius:6px;padding:2px 10px;font-size:.85rem;color:var(--accent2);margin:0 4px 4px 0}
+.stats{display:grid;grid-template-columns:repeat(auto-fit,minmax(160px,1fr));gap:1rem;margin:2rem 0}
+.stat{background:var(--card);border:1px solid var(--border);border-radius:12px;padding:1.25rem;text-align:center}
+.stat-value{font-size:2rem;font-weight:800;background:linear-gradient(135deg,var(--accent),var(--accent2));-webkit-background-clip:text;-webkit-text-fill-color:transparent}
+.stat-label{color:var(--muted);font-size:.85rem;margin-top:.25rem}
+.token-hero{background:linear-gradient(135deg,#1a1a2e,#16213e);border:1px solid var(--accent);border-radius:16px;padding:2rem;margin:2rem 0;text-align:center}
+.token-saved{font-size:3rem;font-weight:900;color:var(--green)}
+.token-detail{color:var(--muted);font-size:.9rem;margin-top:.5rem}
+.section{margin:2.5rem 0}
+.section h2{font-size:1.4rem;font-weight:700;margin-bottom:1rem;padding-bottom:.5rem;border-bottom:1px solid var(--border)}
+.grid{display:grid;grid-template-columns:repeat(auto-fill,minmax(320px,1fr));gap:1rem}
+.card{background:var(--card);border:1px solid var(--border);border-radius:10px;padding:1rem;transition:border-color .2s}
+.card:hover{border-color:var(--accent)}
+.card-title{font-weight:700;font-size:1rem;margin-bottom:.5rem}
+.card-meta{color:var(--muted);font-size:.8rem}
+.tag{display:inline-block;background:rgba(99,102,241,.15);color:var(--accent);border-radius:4px;padding:1px 6px;font-size:.75rem;margin:1px}
+.tag-auth{background:rgba(239,68,68,.15);color:var(--red)}
+.tag-db{background:rgba(34,211,238,.15);color:var(--accent2)}
+.tag-ai{background:rgba(236,72,153,.15);color:var(--pink)}
+.tag-payment{background:rgba(245,158,11,.15);color:var(--orange)}
+.tag-email{background:rgba(34,197,94,.15);color:var(--green)}
+.tag-queue{background:rgba(168,85,247,.15);color:#a855f7}
+.tag-cache{background:rgba(245,158,11,.15);color:var(--orange)}
+.method{font-weight:700;font-size:.8rem;padding:2px 6px;border-radius:4px;margin-right:6px}
+.method-GET{background:rgba(34,197,94,.2);color:var(--green)}
+.method-POST{background:rgba(99,102,241,.2);color:var(--accent)}
+.method-PUT{background:rgba(245,158,11,.2);color:var(--orange)}
+.method-PATCH{background:rgba(245,158,11,.2);color:var(--orange)}
+.method-DELETE{background:rgba(239,68,68,.2);color:var(--red)}
+.method-ALL{background:rgba(107,107,128,.2);color:var(--muted)}
+.route-path{font-family:'Fira Code',monospace;font-size:.9rem}
+.route-contract{color:var(--muted);font-size:.8rem;font-style:italic;margin-left:.5rem}
+.field{display:flex;gap:.5rem;padding:3px 0;font-size:.9rem}
+.field-name{font-family:monospace;color:var(--accent2)}
+.field-type{color:var(--muted);font-family:monospace}
+.field-flags{display:flex;gap:3px}
+.flag{font-size:.7rem;padding:0 4px;border-radius:3px;background:rgba(99,102,241,.1);color:var(--accent)}
+.flag-pk{background:rgba(245,158,11,.2);color:var(--orange)}
+.flag-fk{background:rgba(34,211,238,.2);color:var(--accent2)}
+.flag-unique{background:rgba(236,72,153,.2);color:var(--pink)}
+.hot-bar{height:8px;background:linear-gradient(90deg,var(--accent),var(--accent2));border-radius:4px;margin-top:4px}
+.component-props{color:var(--muted);font-size:.85rem}
+.badge-client{background:rgba(34,197,94,.15);color:var(--green);font-size:.75rem;padding:1px 6px;border-radius:4px}
+.badge-server{background:rgba(99,102,241,.15);color:var(--accent);font-size:.75rem;padding:1px 6px;border-radius:4px}
+.env-required{color:var(--red);font-weight:600;font-size:.8rem}
+.env-default{color:var(--green);font-size:.8rem}
+.footer{text-align:center;color:var(--muted);margin-top:4rem;padding-top:2rem;border-top:1px solid var(--border);font-size:.85rem}
+.footer a{color:var(--accent);text-decoration:none}
+table{width:100%;border-collapse:collapse}
+table td,table th{padding:8px 12px;text-align:left;border-bottom:1px solid var(--border);font-size:.9rem}
+table th{color:var(--muted);font-size:.8rem;font-weight:600;text-transform:uppercase}
+</style>
+</head>
+<body>
+
+<h1>NowChessSystems</h1>
+<div class="subtitle">AI Context Map — generated by codesight</div>
+
+<div>
+<span class="stack-badge">raw-http</span>
+
+<span class="stack-badge">unknown</span>
+<span class="stack-badge">scala</span>
+
+</div>
+
+<div class="token-hero">
+  <div class="token-saved">~20,573 tokens saved</div>
+  <div class="token-detail">
+    Output: 5,297 tokens — Exploration cost without codesight: ~25,870 tokens — 149 files scanned
+  </div>
+</div>
+
+<div class="stats">
+  <div class="stat"><div class="stat-value">0</div><div class="stat-label">Routes</div></div>
+  <div class="stat"><div class="stat-value">0</div><div class="stat-label">Models</div></div>
+  <div class="stat"><div class="stat-value">0</div><div class="stat-label">Components</div></div>
+  <div class="stat"><div class="stat-value">63</div><div class="stat-label">Libraries</div></div>
+  <div class="stat"><div class="stat-value">1</div><div class="stat-label">Env Vars</div></div>
+  <div class="stat"><div class="stat-value">1</div><div class="stat-label">Middleware</div></div>
+  <div class="stat"><div class="stat-value">383</div><div class="stat-label">Import Links</div></div>
+</div>
+
+
+
+
+
+
+
+
+<div class="section">
+<h2>Dependency Hot Files</h2>
+<div class="grid">
+<div class="card">
+  <div class="card-title" style="font-size:.9rem">modules/api/src/main/scala/de/nowchess/api/game/GameContext.scala</div>
+  <div class="card-meta">imported by 64 files</div>
+  <div class="hot-bar" style="width:100%"></div>
+</div>
+<div class="card">
+  <div class="card-title" style="font-size:.9rem">modules/api/src/main/scala/de/nowchess/api/move/Move.scala</div>
+  <div class="card-meta">imported by 44 files</div>
+  <div class="hot-bar" style="width:69%"></div>
+</div>
+<div class="card">
+  <div class="card-title" style="font-size:.9rem">modules/api/src/main/scala/de/nowchess/api/board/Square.scala</div>
+  <div class="card-meta">imported by 40 files</div>
+  <div class="hot-bar" style="width:63%"></div>
+</div>
+<div class="card">
+  <div class="card-title" style="font-size:.9rem">modules/api/src/main/scala/de/nowchess/api/board/Color.scala</div>
+  <div class="card-meta">imported by 35 files</div>
+  <div class="hot-bar" style="width:55%"></div>
+</div>
+<div class="card">
+  <div class="card-title" style="font-size:.9rem">modules/api/src/main/scala/de/nowchess/api/board/Board.scala</div>
+  <div class="card-meta">imported by 19 files</div>
+  <div class="hot-bar" style="width:30%"></div>
+</div>
+<div class="card">
+  <div class="card-title" style="font-size:.9rem">modules/api/src/main/scala/de/nowchess/api/board/Piece.scala</div>
+  <div class="card-meta">imported by 18 files</div>
+  <div class="hot-bar" style="width:28%"></div>
+</div>
+<div class="card">
+  <div class="card-title" style="font-size:.9rem">modules/api/src/main/scala/de/nowchess/api/board/PieceType.scala</div>
+  <div class="card-meta">imported by 17 files</div>
+  <div class="hot-bar" style="width:27%"></div>
+</div>
+<div class="card">
+  <div class="card-title" style="font-size:.9rem">modules/rule/src/main/scala/de/nowchess/rules/sets/DefaultRules.scala</div>
+  <div class="card-meta">imported by 17 files</div>
+  <div class="hot-bar" style="width:27%"></div>
+</div>
+<div class="card">
+  <div class="card-title" style="font-size:.9rem">modules/rule/src/main/scala/de/nowchess/rules/RuleSet.scala</div>
+  <div class="card-meta">imported by 11 files</div>
+  <div class="hot-bar" style="width:17%"></div>
+</div>
+<div class="card">
+  <div class="card-title" style="font-size:.9rem">modules/io/src/main/scala/de/nowchess/io/fen/FenParser.scala</div>
+  <div class="card-meta">imported by 10 files</div>
+  <div class="hot-bar" style="width:16%"></div>
+</div>
+<div class="card">
+  <div class="card-title" style="font-size:.9rem">modules/api/src/main/scala/de/nowchess/api/board/CastlingRights.scala</div>
+  <div class="card-meta">imported by 9 files</div>
+  <div class="hot-bar" style="width:14%"></div>
+</div>
+<div class="card">
+  <div class="card-title" style="font-size:.9rem">modules/api/src/main/scala/de/nowchess/api/game/DrawReason.scala</div>
+  <div class="card-meta">imported by 7 files</div>
+  <div class="hot-bar" style="width:11%"></div>
+</div>
+</div>
+</div>
+
+
+<div class="section">
+<h2>Environment Variables</h2>
+<table>
+<tr><th>Variable</th><th>Status</th><th>Source</th></tr>
+<tr>
+  <td><code>STOCKFISH_PATH</code></td>
+  <td><span class="env-required">required</span></td>
+  <td class="card-meta">modules/bot/python/nnue.py</td>
+</tr>
+</table>
+</div>
+
+
+<div class="section">
+<h2>Middleware</h2>
+<div class="grid">
+<div class="card">
+  <div class="card-title">generate <span class="tag tag-custom">custom</span></div>
+  <div class="card-meta">modules/bot/python/src/generate.py</div>
+</div>
+</div>
+</div>
+
+<div class="footer">
+  Generated by <a href="https://github.com/Houseofmvps/codesight">codesight</a> — see your codebase clearly
+</div>
+
+</body>
+</html>
@@ -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>^$</XML_NAMESPACE>
+                </AND>
+              </match>
+              <order>BY_NAME</order>
+            </rule>
+          </section>
+          <section>
+            <rule>
+              <match>
+                <AND>
+                  <NAME>.*</NAME>
+                  <XML_ATTRIBUTE />
+                  <XML_NAMESPACE>http://schemas.android.com/apk/res/android</XML_NAMESPACE>
+                </AND>
+              </match>
+            </rule>
+          </section>
+          <section>
+            <rule>
+              <match>
+                <AND>
+                  <NAME>.*</NAME>
+                  <XML_ATTRIBUTE />
+                  <XML_NAMESPACE>.*</XML_NAMESPACE>
+                </AND>
+              </match>
+              <order>BY_NAME</order>
+            </rule>
+          </section>
+        </rules>
+      </arrangement>
+    </codeStyleSettings>
+    <codeStyleSettings language="kotlin">
+      <option name="CODE_STYLE_DEFAULTS" value="KOTLIN_OFFICIAL" />
+    </codeStyleSettings>
+  </code_scheme>
+</component>
@@ -1,5 +1,5 @@
 <component name="ProjectCodeStyleConfiguration">
  <state>
-    <option name="PREFERRED_PROJECT_CODE_STYLE" value="Default" />
+    <option name="USE_PER_PROJECT_SETTINGS" value="true" />
  </state>
 </component>
@@ -11,10 +11,10 @@
            <option value="$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$/modules/rule" />
-            <option value="$PROJECT_DIR$/modules/ui" />
          </set>
        </option>
      </GradleProjectSettings>
@@ -0,0 +1,33 @@
+<component name="ProjectRunConfigurationManager">
+  <configuration default="false" name="NowChessSystems.modules.core.main" type="QuarkusRunConfigurationType" factoryName="Quarkus" nameIsGenerated="true">
+    <module name="NowChessSystems.modules.core.main" />
+    <QsGradleRunConfiguration>
+      <ExternalSystemSettings>
+        <option name="executionName" />
+        <option name="externalProjectPath" value="$PROJECT_DIR$/modules/core" />
+        <option name="externalSystemIdString" value="GRADLE" />
+        <option name="scriptParameters" />
+        <option name="taskDescriptions">
+          <list />
+        </option>
+        <option name="taskNames">
+          <list>
+            <option value="quarkusDev" />
+          </list>
+        </option>
+        <option name="vmOptions" />
+      </ExternalSystemSettings>
+      <ExternalSystemDebugServerProcess>true</ExternalSystemDebugServerProcess>
+      <ExternalSystemReattachDebugProcess>true</ExternalSystemReattachDebugProcess>
+      <ExternalSystemDebugDisabled>false</ExternalSystemDebugDisabled>
+      <DebugAllEnabled>false</DebugAllEnabled>
+      <RunAsTest>false</RunAsTest>
+      <GradleProfilingDisabled>false</GradleProfilingDisabled>
+      <GradleCoverageDisabled>false</GradleCoverageDisabled>
+      <profile>dev</profile>
+    </QsGradleRunConfiguration>
+    <method v="2">
+      <option name="Make" enabled="true" />
+    </method>
+  </configuration>
+</component>
@@ -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.integrationTest,NowChessSystems.modules.core.main,NowChessSystems.modules.core.native-test,NowChessSystems.modules.core.quarkus-generated-sources,NowChessSystems.modules.core.quarkus-test-generated-sources,NowChessSystems.modules.core.scoverage,NowChessSystems.modules.core.test,NowChessSystems.modules.io.integrationTest,NowChessSystems.modules.io.main,NowChessSystems.modules.io.native-test,NowChessSystems.modules.io.quarkus-generated-sources,NowChessSystems.modules.io.quarkus-test-generated-sources,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>
@@ -9,8 +9,9 @@ Scala 3.5.1 · Gradle 9
 ./compile    # Compile all modules — always run
 ./test       # Run all tests
 ./coverage   # Check coverage
+./lint       # Run linters
 ```
-Try to stick to these commands for consistency.
+Use consistently.

 ## Modules

@@ -19,19 +20,20 @@ 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 |

 ## Style

- Use immutable data and pure functions.
- Keep functions under 30 lines. If you need "and" to describe it, split it.
- Keep cyclomatic complexity under 15.
- Avoid comments. Let names carry intent; comment only non-obvious algorithms.
- Scan for duplicated logic before finishing. Extract it.
+- Immutable data, pure functions.
+- Functions under 30 lines. Need "and"? Split it.
+- Cyclomatic complexity under 15.
+- No comments. Names carry intent. Comment non-obvious algorithms only.
+- Scan duplicated logic. Extract.
 - Follow default Sonar style for Scala.
- Use `Option` or `Either` for fallible operations; avoid exceptions for control flow.
- Naming: types are PascalCase, functions/values are camelCase.
+- `Option`/`Either` for fallible ops. Skip exceptions for control flow.
+- Naming: types PascalCase, functions/values camelCase.

 ## Code Quality

@@ -39,20 +41,23 @@ Try to stick to these commands for consistency.

 ### Linters

- **scalafmt** — enforces formatting; run `./gradlew spotlessScalaCheck` to check and `./gradlew spotlessScalaApply` to refactor.
- **scalafix** — enforces style and detects unused imports/code; run `./gradlew scalafix` to apply rules.
+- **scalafmt** — Enforces formatting. Check: `./gradlew spotlessScalaCheck`. Refactor: `./gradlew spotlessScalaApply`.
+- **scalafix** — Enforces style, detects unused imports/code. Run: `./gradlew scalafix`.

 ## Architecture Decisions

- **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.
+- **Immutable state as primary model:** GameContext (api) holds board, history, player state—immutable throughout. Each move → new GameContext. Enables undo/redo without side effects.
+- **Observer pattern for UI decoupling:** GameEngine publishes move/state events; CommandInvoker queues moves; UI listens (no polling). GameEngine never imports UI.
+- **RuleSet trait encapsulates rules:** Move generation, check, castling, en passant all in RuleSet impl. GameEngine calls rules as black box; rules don't know rest of core.
+- **Polyglot hash must follow spec index layout:** Piece keys use interleaved mapping `(pieceType * 2 + colorBit)` (black=0, white=1). Castling keys: `768..771`. En-passant file keys: `772..779`, XORed only if side-to-move has capturable en passant. Side-to-move key: `780` (white).
+- **Alpha-beta uses sequential PV search by default:** Parallel split disabled (fixed-window futures removed pruning effectiveness). Sequential PV default. Correctness + pruning quality > speculative parallelism.
+- **Search hash is updated incrementally per move:** Bot search updates Zobrist keys from parent hash with move deltas, not recomputing piece scans per node.

 ## Rules

- **Tests are the spec.** Never modify tests to pass; modify requirements or code. Update tests only if requirements change.
+- **Tests are the spec.** Don't modify to pass. Fix requirements/code. Update only if requirements change.
 - Never read build folders. Ask permission if needed.
- Keep this file up to date with any important decisions or conventions.
+- Keep file current with decisions + conventions.

 ---

@@ -60,11 +65,9 @@ Try to stick to these commands for consistency.

 ### Two-Step Rule (mandatory)
 **Step 1 — Orient:** Use wiki articles to find WHERE things live.
-**Step 2 — Verify:** Read the actual source files listed in the wiki article BEFORE writing any code.
+**Step 2 — Verify:** Read source files from wiki BEFORE coding.

-Wiki articles are structural summaries extracted by AST. They show routes, models, and file locations.
-They do NOT show full function logic, middleware internals, or dynamic runtime behavior.
-**Never write or modify code based solely on wiki content — always read source files first.**
+Wiki = structural summaries (routes, models, file locations). No function logic, middleware internals, runtime behavior. Don't code from wiki alone—read sources.

 Read in order at session start:
 1. `.codesight/wiki/index.md` — orientation map (~200 tokens)
@@ -72,8 +75,7 @@ Read in order at session start:
 3. Domain article (e.g. `.codesight/wiki/auth.md`) → check "Source Files" section → read those files
 4. `.codesight/CODESIGHT.md` — full context map for deep exploration

-Routes marked `[inferred]` in wiki articles were detected via regex — verify against source before trusting.
-If any source file shows ⚠ in the wiki, re-run `codesight --wiki` before proceeding.
+`[inferred]` routes = regex-detected. Verify sources. ⚠ in wiki? Re-run `codesight --wiki`.

 Or use the codesight MCP server for on-demand queries:
 - `codesight_get_wiki_article` — read a specific wiki article by name
@@ -83,13 +85,13 @@ Or use the codesight MCP server for on-demand queries:
 - `codesight_get_blast_radius --file src/lib/db.ts` — impact analysis before changes
 - `codesight_get_schema --model users` — specific model details

-Only open specific files after consulting codesight context. This saves ~16.893 tokens per conversation.
+Consult codesight context first. Saves ~16.893 tokens/conversation.

 ## graphify

-This project has a graphify knowledge graph at graphify-out/.
+graphify knowledge graph at graphify-out/.

 Rules:
- Before answering architecture or codebase questions, read graphify-out/GRAPH_REPORT.md for god nodes and community structure
- If graphify-out/wiki/index.md exists, navigate it instead of reading raw files
- After modifying code files in this session, run `python3 -c "from graphify.watch import _rebuild_code; from pathlib import Path; _rebuild_code(Path('.'))"` to keep the graph current
+- Architecture/codebase questions? Read graphify-out/GRAPH_REPORT.md (god nodes, communities).
+- graphify-out/wiki/index.md exists? Use it (not raw files).
+- Code modified? Run `python3 -c "from graphify.watch import _rebuild_code; from pathlib import Path; _rebuild_code(Path('.'))"` to sync graph.
@@ -0,0 +1,100 @@
+# Now-Chess
+
+Scala 3.5.1 · Gradle 9
+
+## Commands
+
+```
+./clean      # Clear build dirs — only when necessary
+./compile    # Compile all modules — always run
+./test       # Run all tests
+./coverage   # Check coverage
+./lint       # Run linters
+```
+Try to stick to these commands for consistency.
+
+## Modules
+
+| Module | Role | Depends on |
+|--------|------|-----------|
+| `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 |
+
+## Style
+
+- Use immutable data and pure functions.
+- Keep functions under 30 lines. If you need "and" to describe it, split it.
+- Keep cyclomatic complexity under 15.
+- Avoid comments. Let names carry intent; comment only non-obvious algorithms.
+- Scan for duplicated logic before finishing. Extract it.
+- Follow default Sonar style for Scala.
+- Use `Option` or `Either` for fallible operations; avoid exceptions for control flow.
+- Naming: types are PascalCase, functions/values are camelCase.
+
+## Code Quality
+
+- **Coverage:** 100% condition coverage required in `api`, `core`, `rule`, `io` (mandatory); `ui` exempt.
+
+### Linters
+
+- **scalafmt** — enforces formatting; run `./gradlew spotlessScalaCheck` to check and `./gradlew spotlessScalaApply` to refactor.
+- **scalafix** — enforces style and detects unused imports/code; run `./gradlew scalafix` to apply rules.
+
+## Architecture Decisions
+
+- **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
+
+- **Tests are the spec.** Never modify tests to pass; modify requirements or code. Update tests only if requirements change.
+- Never read build folders. Ask permission if needed.
+- Keep this file up to date with any important decisions or conventions.
+
+---
+
+## Instructions for Claude Code
+
+### Two-Step Rule (mandatory)
+**Step 1 — Orient:** Use wiki articles to find WHERE things live.
+**Step 2 — Verify:** Read the actual source files listed in the wiki article BEFORE writing any code.
+
+Wiki articles are structural summaries extracted by AST. They show routes, models, and file locations.
+They do NOT show full function logic, middleware internals, or dynamic runtime behavior.
+**Never write or modify code based solely on wiki content — always read source files first.**
+
+Read in order at session start:
+1. `.codesight/wiki/index.md` — orientation map (~200 tokens)
+2. `.codesight/wiki/overview.md` — architecture overview (~500 tokens)
+3. Domain article (e.g. `.codesight/wiki/auth.md`) → check "Source Files" section → read those files
+4. `.codesight/CODESIGHT.md` — full context map for deep exploration
+
+Routes marked `[inferred]` in wiki articles were detected via regex — verify against source before trusting.
+If any source file shows ⚠ in the wiki, re-run `codesight --wiki` before proceeding.
+
+Or use the codesight MCP server for on-demand queries:
+- `codesight_get_wiki_article` — read a specific wiki article by name
+- `codesight_get_wiki_index` — get the wiki index
+- `codesight_get_summary` — quick project overview
+- `codesight_get_routes --prefix /api/users` — filtered routes
+- `codesight_get_blast_radius --file src/lib/db.ts` — impact analysis before changes
+- `codesight_get_schema --model users` — specific model details
+
+Only open specific files after consulting codesight context. This saves ~16.893 tokens per conversation.
+
+## graphify
+
+This project has a graphify knowledge graph at graphify-out/.
+
+Rules:
+- Before answering architecture or codebase questions, read graphify-out/GRAPH_REPORT.md for god nodes and community structure
+- If graphify-out/wiki/index.md exists, navigate it instead of reading raw files
+- After modifying code files in this session, run `python3 -c "from graphify.watch import _rebuild_code; from pathlib import Path; _rebuild_code(Path('.'))"` to keep the graph current
@@ -0,0 +1,12 @@
+meta {
+  name: Offer Draw
+  type: http
+  seq: 1
+}
+
+http {
+  method: POST
+  url: {{baseUrl}}/api/board/game/{{gameId}}/draw/offer
+  body: none
+  auth: none
+}
@@ -0,0 +1,12 @@
+meta {
+  name: Accept Draw
+  type: http
+  seq: 2
+}
+
+http {
+  method: POST
+  url: {{baseUrl}}/api/board/game/{{gameId}}/draw/accept
+  body: none
+  auth: none
+}
@@ -0,0 +1,12 @@
+meta {
+  name: Decline Draw
+  type: http
+  seq: 3
+}
+
+http {
+  method: POST
+  url: {{baseUrl}}/api/board/game/{{gameId}}/draw/decline
+  body: none
+  auth: none
+}
@@ -0,0 +1,12 @@
+meta {
+  name: Claim Draw
+  type: http
+  seq: 4
+}
+
+http {
+  method: POST
+  url: {{baseUrl}}/api/board/game/{{gameId}}/draw/claim
+  body: none
+  auth: none
+}
@@ -0,0 +1,4 @@
+meta {
+  name: draw
+  seq: 2
+}
@@ -0,0 +1,12 @@
+meta {
+  name: Export FEN
+  type: http
+  seq: 1
+}
+
+http {
+  method: GET
+  url: {{baseUrl}}/api/board/game/{{gameId}}/export/fen
+  body: none
+  auth: none
+}
@@ -0,0 +1,12 @@
+meta {
+  name: Export PGN
+  type: http
+  seq: 2
+}
+
+http {
+  method: GET
+  url: {{baseUrl}}/api/board/game/{{gameId}}/export/pgn
+  body: none
+  auth: none
+}
@@ -0,0 +1,4 @@
+meta {
+  name: export
+  seq: 6
+}
@@ -0,0 +1,23 @@
+meta {
+  name: Create Game
+  type: http
+  seq: 1
+}
+
+http {
+  method: POST
+  url: {{baseUrl}}/api/board/game
+  body: json
+  auth: none
+}
+
+headers {
+  Content-Type: application/json
+}
+
+body:json {
+  {
+    "white": {"id": "p1", "displayName": "Alice"},
+    "black": {"id": "p2", "displayName": "Bob"}
+  }
+}
@@ -0,0 +1,12 @@
+meta {
+  name: Get Game
+  type: http
+  seq: 2
+}
+
+http {
+  method: GET
+  url: {{baseUrl}}/api/board/game/{{gameId}}
+  body: none
+  auth: none
+}
@@ -0,0 +1,19 @@
+meta {
+  name: Stream Game
+  type: http
+  seq: 3
+}
+
+get {
+  url: {{baseUrl}}/api/board/game/{{gameId}}/stream
+  body: none
+  auth: none
+}
+
+headers {
+  Accept: application/x-ndjson
+}
+
+vars:pre-request {
+  gameId: tjOgyEcS
+}
@@ -0,0 +1,12 @@
+meta {
+  name: Resign
+  type: http
+  seq: 4
+}
+
+http {
+  method: POST
+  url: {{baseUrl}}/api/board/game/{{gameId}}/resign
+  body: none
+  auth: none
+}
@@ -0,0 +1,4 @@
+meta {
+  name: game
+  seq: 3
+}
@@ -0,0 +1,24 @@
+meta {
+  name: Import FEN
+  type: http
+  seq: 1
+}
+
+http {
+  method: POST
+  url: {{baseUrl}}/api/board/game/import/fen
+  body: json
+  auth: none
+}
+
+headers {
+  Content-Type: application/json
+}
+
+body:json {
+  {
+    "fen": "rnbqkbnr/pppppppp/8/8/4P3/8/PPPP1PPP/RNBQKBNR b KQkq e3 0 1",
+    "white": {"id": "p1", "displayName": "Alice"},
+    "black": {"id": "p2", "displayName": "Bob"}
+  }
+}
@@ -0,0 +1,22 @@
+meta {
+  name: Import PGN
+  type: http
+  seq: 2
+}
+
+http {
+  method: POST
+  url: {{baseUrl}}/api/board/game/import/pgn
+  body: json
+  auth: none
+}
+
+headers {
+  Content-Type: application/json
+}
+
+body:json {
+  {
+    "pgn": "1. e4 e5 2. Nf3 Nc6 *"
+  }
+}
@@ -0,0 +1,4 @@
+meta {
+  name: import
+  seq: 5
+}
@@ -0,0 +1,15 @@
+meta {
+  name: Make Move
+  type: http
+  seq: 1
+}
+
+post {
+  url: {{baseUrl}}/api/board/game/{{gameId}}/move/b1c3
+  body: none
+  auth: none
+}
+
+vars:pre-request {
+  gameId: tjOgyEcS
+}
@@ -0,0 +1,19 @@
+meta {
+  name: Get Legal Moves
+  type: http
+  seq: 2
+}
+
+get {
+  url: {{baseUrl}}/api/board/game/{{gameId}}/moves
+  body: none
+  auth: none
+}
+
+params:query {
+  square: e2
+}
+
+vars:pre-request {
+  gameId: tjOgyEcS
+}
@@ -0,0 +1,12 @@
+meta {
+  name: Undo Move
+  type: http
+  seq: 3
+}
+
+http {
+  method: POST
+  url: {{baseUrl}}/api/board/game/{{gameId}}/undo
+  body: none
+  auth: none
+}
@@ -0,0 +1,12 @@
+meta {
+  name: Redo Move
+  type: http
+  seq: 4
+}
+
+http {
+  method: POST
+  url: {{baseUrl}}/api/board/game/{{gameId}}/redo
+  body: none
+  auth: none
+}
@@ -0,0 +1,3 @@
+meta {
+  name: move
+}
@@ -0,0 +1,6 @@
+{
+  "version": "1",
+  "name": "NowChess API",
+  "type": "collection",
+  "ignore": []
+}
@@ -0,0 +1,4 @@
+vars {
+  baseUrl: http://localhost:8080
+  ioBaseUrl: http://localhost:8081
+}
@@ -0,0 +1,100 @@
+meta {
+  name: Export FEN
+  type: http
+  seq: 1
+}
+
+http {
+  method: POST
+  url: {{ioBaseUrl}}/io/export/fen
+  body: json
+  auth: none
+}
+
+headers {
+  Content-Type: application/json
+}
+
+body:json {
+  {
+    "board": {
+      "a1": {"color": "White", "pieceType": "Rook"},
+      "b1": {"color": "White", "pieceType": "Knight"},
+      "c1": {"color": "White", "pieceType": "Bishop"},
+      "d1": {"color": "White", "pieceType": "Queen"},
+      "e1": {"color": "White", "pieceType": "King"},
+      "f1": {"color": "White", "pieceType": "Bishop"},
+      "g1": {"color": "White", "pieceType": "Knight"},
+      "h1": {"color": "White", "pieceType": "Rook"},
+      "a2": {"color": "White", "pieceType": "Pawn"},
+      "b2": {"color": "White", "pieceType": "Pawn"},
+      "c2": {"color": "White", "pieceType": "Pawn"},
+      "d2": {"color": "White", "pieceType": "Pawn"},
+      "e2": {"color": "White", "pieceType": "Pawn"},
+      "f2": {"color": "White", "pieceType": "Pawn"},
+      "g2": {"color": "White", "pieceType": "Pawn"},
+      "h2": {"color": "White", "pieceType": "Pawn"},
+      "a7": {"color": "Black", "pieceType": "Pawn"},
+      "b7": {"color": "Black", "pieceType": "Pawn"},
+      "c7": {"color": "Black", "pieceType": "Pawn"},
+      "d7": {"color": "Black", "pieceType": "Pawn"},
+      "e7": {"color": "Black", "pieceType": "Pawn"},
+      "f7": {"color": "Black", "pieceType": "Pawn"},
+      "g7": {"color": "Black", "pieceType": "Pawn"},
+      "h7": {"color": "Black", "pieceType": "Pawn"},
+      "a8": {"color": "Black", "pieceType": "Rook"},
+      "b8": {"color": "Black", "pieceType": "Knight"},
+      "c8": {"color": "Black", "pieceType": "Bishop"},
+      "d8": {"color": "Black", "pieceType": "Queen"},
+      "e8": {"color": "Black", "pieceType": "King"},
+      "f8": {"color": "Black", "pieceType": "Bishop"},
+      "g8": {"color": "Black", "pieceType": "Knight"},
+      "h8": {"color": "Black", "pieceType": "Rook"}
+    },
+    "turn": "White",
+    "castlingRights": {
+      "whiteKingSide": true,
+      "whiteQueenSide": true,
+      "blackKingSide": true,
+      "blackQueenSide": true
+    },
+    "enPassantSquare": null,
+    "halfMoveClock": 0,
+    "moves": [],
+    "result": null,
+    "initialBoard": {
+      "a1": {"color": "White", "pieceType": "Rook"},
+      "b1": {"color": "White", "pieceType": "Knight"},
+      "c1": {"color": "White", "pieceType": "Bishop"},
+      "d1": {"color": "White", "pieceType": "Queen"},
+      "e1": {"color": "White", "pieceType": "King"},
+      "f1": {"color": "White", "pieceType": "Bishop"},
+      "g1": {"color": "White", "pieceType": "Knight"},
+      "h1": {"color": "White", "pieceType": "Rook"},
+      "a2": {"color": "White", "pieceType": "Pawn"},
+      "b2": {"color": "White", "pieceType": "Pawn"},
+      "c2": {"color": "White", "pieceType": "Pawn"},
+      "d2": {"color": "White", "pieceType": "Pawn"},
+      "e2": {"color": "White", "pieceType": "Pawn"},
+      "f2": {"color": "White", "pieceType": "Pawn"},
+      "g2": {"color": "White", "pieceType": "Pawn"},
+      "h2": {"color": "White", "pieceType": "Pawn"},
+      "a7": {"color": "Black", "pieceType": "Pawn"},
+      "b7": {"color": "Black", "pieceType": "Pawn"},
+      "c7": {"color": "Black", "pieceType": "Pawn"},
+      "d7": {"color": "Black", "pieceType": "Pawn"},
+      "e7": {"color": "Black", "pieceType": "Pawn"},
+      "f7": {"color": "Black", "pieceType": "Pawn"},
+      "g7": {"color": "Black", "pieceType": "Pawn"},
+      "h7": {"color": "Black", "pieceType": "Pawn"},
+      "a8": {"color": "Black", "pieceType": "Rook"},
+      "b8": {"color": "Black", "pieceType": "Knight"},
+      "c8": {"color": "Black", "pieceType": "Bishop"},
+      "d8": {"color": "Black", "pieceType": "Queen"},
+      "e8": {"color": "Black", "pieceType": "King"},
+      "f8": {"color": "Black", "pieceType": "Bishop"},
+      "g8": {"color": "Black", "pieceType": "Knight"},
+      "h8": {"color": "Black", "pieceType": "Rook"}
+    }
+  }
+}
@@ -0,0 +1,100 @@
+meta {
+  name: Export PGN
+  type: http
+  seq: 2
+}
+
+http {
+  method: POST
+  url: {{ioBaseUrl}}/io/export/pgn
+  body: json
+  auth: none
+}
+
+headers {
+  Content-Type: application/json
+}
+
+body:json {
+  {
+    "board": {
+      "a1": {"color": "White", "pieceType": "Rook"},
+      "b1": {"color": "White", "pieceType": "Knight"},
+      "c1": {"color": "White", "pieceType": "Bishop"},
+      "d1": {"color": "White", "pieceType": "Queen"},
+      "e1": {"color": "White", "pieceType": "King"},
+      "f1": {"color": "White", "pieceType": "Bishop"},
+      "g1": {"color": "White", "pieceType": "Knight"},
+      "h1": {"color": "White", "pieceType": "Rook"},
+      "a2": {"color": "White", "pieceType": "Pawn"},
+      "b2": {"color": "White", "pieceType": "Pawn"},
+      "c2": {"color": "White", "pieceType": "Pawn"},
+      "d2": {"color": "White", "pieceType": "Pawn"},
+      "e2": {"color": "White", "pieceType": "Pawn"},
+      "f2": {"color": "White", "pieceType": "Pawn"},
+      "g2": {"color": "White", "pieceType": "Pawn"},
+      "h2": {"color": "White", "pieceType": "Pawn"},
+      "a7": {"color": "Black", "pieceType": "Pawn"},
+      "b7": {"color": "Black", "pieceType": "Pawn"},
+      "c7": {"color": "Black", "pieceType": "Pawn"},
+      "d7": {"color": "Black", "pieceType": "Pawn"},
+      "e7": {"color": "Black", "pieceType": "Pawn"},
+      "f7": {"color": "Black", "pieceType": "Pawn"},
+      "g7": {"color": "Black", "pieceType": "Pawn"},
+      "h7": {"color": "Black", "pieceType": "Pawn"},
+      "a8": {"color": "Black", "pieceType": "Rook"},
+      "b8": {"color": "Black", "pieceType": "Knight"},
+      "c8": {"color": "Black", "pieceType": "Bishop"},
+      "d8": {"color": "Black", "pieceType": "Queen"},
+      "e8": {"color": "Black", "pieceType": "King"},
+      "f8": {"color": "Black", "pieceType": "Bishop"},
+      "g8": {"color": "Black", "pieceType": "Knight"},
+      "h8": {"color": "Black", "pieceType": "Rook"}
+    },
+    "turn": "White",
+    "castlingRights": {
+      "whiteKingSide": true,
+      "whiteQueenSide": true,
+      "blackKingSide": true,
+      "blackQueenSide": true
+    },
+    "enPassantSquare": null,
+    "halfMoveClock": 0,
+    "moves": [],
+    "result": null,
+    "initialBoard": {
+      "a1": {"color": "White", "pieceType": "Rook"},
+      "b1": {"color": "White", "pieceType": "Knight"},
+      "c1": {"color": "White", "pieceType": "Bishop"},
+      "d1": {"color": "White", "pieceType": "Queen"},
+      "e1": {"color": "White", "pieceType": "King"},
+      "f1": {"color": "White", "pieceType": "Bishop"},
+      "g1": {"color": "White", "pieceType": "Knight"},
+      "h1": {"color": "White", "pieceType": "Rook"},
+      "a2": {"color": "White", "pieceType": "Pawn"},
+      "b2": {"color": "White", "pieceType": "Pawn"},
+      "c2": {"color": "White", "pieceType": "Pawn"},
+      "d2": {"color": "White", "pieceType": "Pawn"},
+      "e2": {"color": "White", "pieceType": "Pawn"},
+      "f2": {"color": "White", "pieceType": "Pawn"},
+      "g2": {"color": "White", "pieceType": "Pawn"},
+      "h2": {"color": "White", "pieceType": "Pawn"},
+      "a7": {"color": "Black", "pieceType": "Pawn"},
+      "b7": {"color": "Black", "pieceType": "Pawn"},
+      "c7": {"color": "Black", "pieceType": "Pawn"},
+      "d7": {"color": "Black", "pieceType": "Pawn"},
+      "e7": {"color": "Black", "pieceType": "Pawn"},
+      "f7": {"color": "Black", "pieceType": "Pawn"},
+      "g7": {"color": "Black", "pieceType": "Pawn"},
+      "h7": {"color": "Black", "pieceType": "Pawn"},
+      "a8": {"color": "Black", "pieceType": "Rook"},
+      "b8": {"color": "Black", "pieceType": "Knight"},
+      "c8": {"color": "Black", "pieceType": "Bishop"},
+      "d8": {"color": "Black", "pieceType": "Queen"},
+      "e8": {"color": "Black", "pieceType": "King"},
+      "f8": {"color": "Black", "pieceType": "Bishop"},
+      "g8": {"color": "Black", "pieceType": "Knight"},
+      "h8": {"color": "Black", "pieceType": "Rook"}
+    }
+  }
+}
@@ -0,0 +1,4 @@
+meta {
+  name: export
+  seq: 2
+}
@@ -0,0 +1,22 @@
+meta {
+  name: Import FEN
+  type: http
+  seq: 1
+}
+
+http {
+  method: POST
+  url: {{ioBaseUrl}}/io/import/fen
+  body: json
+  auth: none
+}
+
+headers {
+  Content-Type: application/json
+}
+
+body:json {
+  {
+    "fen": "rnbqkbnr/pppppppp/8/8/4P3/8/PPPP1PPP/RNBQKBNR b KQkq e3 0 1"
+  }
+}
@@ -0,0 +1,22 @@
+meta {
+  name: Import PGN
+  type: http
+  seq: 2
+}
+
+http {
+  method: POST
+  url: {{ioBaseUrl}}/io/import/pgn
+  body: json
+  auth: none
+}
+
+headers {
+  Content-Type: application/json
+}
+
+body:json {
+  {
+    "pgn": "1. e4 e5 2. Nf3 Nc6 *"
+  }
+}
@@ -0,0 +1,4 @@
+meta {
+  name: import
+  seq: 1
+}
@@ -8,6 +8,53 @@ plugins {
 group = "de.nowchess"
 version = "1.0-SNAPSHOT"

+// Canonical coverage exclusions — glob patterns consumed by Sonar directly;
+// converted to scoverage regexes via globToScoverageRegex for instrumentation-time exclusion.
+val coverageExclusions = listOf(
+    // 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",
+    // DTO case class synthetic methods (Scala compiler-generated apply/$default params)
+    "**/api/src/main/scala/de/nowchess/api/dto/**Dto.scala",
+    // Core infrastructure: exception classes, config, registry implementation, game entry
+    "**/core/src/main/scala/de/nowchess/chess/exception/**",
+    "**/core/src/main/scala/de/nowchess/chess/config/**",
+    "**/core/src/main/scala/de/nowchess/chess/registry/GameEntry.scala",
+    "**/core/src/main/scala/de/nowchess/chess/registry/GameRegistryImpl.scala",
+    // GameResource — REST integration layer with @Inject var fields; mocking dependencies for unit tests is infeasible with Quarkus DI; integration tests would require @QuarkusTest which Scoverage doesn't instrument
+    "**/core/src/main/scala/de/nowchess/chess/resource/GameResource.scala",
+    // IoResource — same rationale as GameResource; @QuarkusTest not instrumented by Scoverage
+    "**/io/src/main/scala/de/nowchess/io/service/resource/IoResource.scala",
+    // JacksonConfig — Quarkus lifecycle hook, no testable logic beyond ObjectMapper registration
+    "**/io/src/main/scala/de/nowchess/io/service/config/JacksonConfig.scala",
+)
+
+// Converts a Sonar-style glob to a scoverage regex (matched against full source path).
+// Order matters: protect ** before converting lone *, escape dots last.
+fun globToScoverageRegex(glob: String): String =
+    glob
+        .replace("**", "^@")
+        .replace("*", "[^/]*")
+        .replace(".", "\\.")
+        .replace("^@", ".*")
+        .let { ".*$it" }
+
+extra["SCOVERAGE_EXCLUDED"] = coverageExclusions.map(::globToScoverageRegex)
+
 sonar {
    properties {
        property("sonar.projectKey", "Now-Chess-Systems")
@@ -22,19 +69,21 @@ sonar {
        }.joinToString(",")

        property("sonar.scala.coverage.reportPaths", scoverageReports)
+        property("sonar.coverage.exclusions", coverageExclusions.joinToString(","))
    }
 }

 val versions = mapOf(
    "QUARKUS_SCALA3"        to "1.0.0",
    "SCALA3"                to "3.5.1",
-    "SCALA_LIBRARY"         to "2.13.18",
+    "SCALA_LIBRARY"         to "2.13.16",
    "SCALATEST"             to "3.2.19",
    "SCALATEST_JUNIT"       to "0.1.11",
    "SCOVERAGE"             to "2.1.1",
    "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",
@@ -1,6 +1,6 @@
 openapi: 3.0.3
 info:
-  title: NowChess API
+  title: NowChess Board API
  description: |
    REST API for the NowChess application. Designed to feel familiar to users
    of the [lichess API](https://lichess.org/api).
@@ -186,11 +186,8 @@ paths:
        currently to move.

        For promotion moves include the target piece as the fifth character:
-        `e7e8q`, `a2a1r`, etc.
-
-        If the move results in a pawn reaching the back rank and no promotion
-        character is supplied, the game enters `promotionPending` status and
-        the move is not yet applied — resubmit with the promotion character.
+        `e7e8q`, `a2a1r`, etc. Promotion moves without the fifth character
+        are rejected with `400 INVALID_MOVE`.
      security:
        - bearerAuth: []
      parameters:
@@ -630,7 +627,6 @@ components:
        | `draw` | Draw agreed or claimed — game over |
        | `drawOffered` | Waiting for the opponent to accept or decline a draw offer |
        | `fiftyMoveAvailable` | Fifty-move rule threshold reached; active player may claim draw |
-        | `promotionPending` | A pawn reached the back rank; awaiting promotion piece selection |
        | `insufficientMaterial` | Neither side has enough pieces to deliver checkmate — game over (draw) |
      enum:
        - started
@@ -641,7 +637,6 @@ components:
        - draw
        - drawOffered
        - fiftyMoveAvailable
-        - promotionPending
        - insufficientMaterial

    # -------------------------------------------------------------------------
@@ -0,0 +1,6 @@
+# Gradle properties
+quarkusPluginId=io.quarkus
+quarkusPluginVersion=3.32.4
+quarkusPlatformGroupId=io.quarkus.platform
+quarkusPlatformArtifactId=quarkus-bom
+quarkusPlatformVersion=3.32.4
@@ -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
@@ -0,0 +1,3 @@
+#! /usr/bin/env bash
+
+./gradlew scalafix spotlessCheck
@@ -34,3 +34,39 @@
 * 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))
+##  (2026-04-19)
+
+### 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))
+* NCS-41 Bot Platform ([#33](https://git.janis-eccarius.de/NowChess/NowChessSystems/issues/33)) ([dceab08](https://git.janis-eccarius.de/NowChess/NowChessSystems/commit/dceab0875e6d15f7d3958633cf5dd5b29a851b1d))
+##  (2026-04-19)
+
+### 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))
+* NCS-41 Bot Platform ([#33](https://git.janis-eccarius.de/NowChess/NowChessSystems/issues/33)) ([dceab08](https://git.janis-eccarius.de/NowChess/NowChessSystems/commit/dceab0875e6d15f7d3958633cf5dd5b29a851b1d))
+##  (2026-04-21)
+
+### 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))
+* NCS-37 Quarkus integration ([#35](https://git.janis-eccarius.de/NowChess/NowChessSystems/issues/35)) ([5ad5efb](https://git.janis-eccarius.de/NowChess/NowChessSystems/commit/5ad5efb41e9df9e3dccb48f96a69f06217ab98e1))
+* NCS-41 Bot Platform ([#33](https://git.janis-eccarius.de/NowChess/NowChessSystems/issues/33)) ([dceab08](https://git.janis-eccarius.de/NowChess/NowChessSystems/commit/dceab0875e6d15f7d3958633cf5dd5b29a851b1d))
@@ -8,6 +8,8 @@ version = "1.0-SNAPSHOT"

@Suppress("UNCHECKED_CAST")
 val versions = rootProject.extra["VERSIONS"] as Map<String, String>
+@Suppress("UNCHECKED_CAST")
+val scoverageExcluded = rootProject.extra["SCOVERAGE_EXCLUDED"] as List<String>

 repositories {
    mavenCentral()
@@ -19,6 +21,7 @@ scala {

 scoverage {
    scoverageVersion.set(versions["SCOVERAGE"]!!)
+    excludedFiles.set(scoverageExcluded)
 }

 configurations.scoverage {
@@ -31,7 +34,7 @@ configurations.scoverage {

 dependencies {

-    implementation("org.scala-lang:scala3-compiler_3") {
+    compileOnly("org.scala-lang:scala3-compiler_3") {
        version {
            strictly(versions["SCALA3"]!!)
        }
@@ -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]
+
+}
@@ -0,0 +1,3 @@
+package de.nowchess.api.dto
+
+final case class ApiErrorDto(code: String, message: String, field: Option[String])
@@ -0,0 +1,6 @@
+package de.nowchess.api.dto
+
+final case class CreateGameRequestDto(
+    white: Option[PlayerInfoDto],
+    black: Option[PlayerInfoDto],
+)
@@ -0,0 +1,6 @@
+package de.nowchess.api.dto
+
+final case class ErrorEventDto(`type`: String, error: ApiErrorDto)
+
+object ErrorEventDto:
+  def apply(error: ApiErrorDto): ErrorEventDto = ErrorEventDto("error", error)
@@ -0,0 +1,8 @@
+package de.nowchess.api.dto
+
+final case class GameFullDto(
+    gameId: String,
+    white: PlayerInfoDto,
+    black: PlayerInfoDto,
+    state: GameStateDto,
+)
@@ -0,0 +1,6 @@
+package de.nowchess.api.dto
+
+final case class GameFullEventDto(`type`: String, game: GameFullDto)
+
+object GameFullEventDto:
+  def apply(game: GameFullDto): GameFullEventDto = GameFullEventDto("gameFull", game)
@@ -0,0 +1,12 @@
+package de.nowchess.api.dto
+
+final case class GameStateDto(
+    fen: String,
+    pgn: String,
+    turn: String,
+    status: String,
+    winner: Option[String],
+    moves: List[String],
+    undoAvailable: Boolean,
+    redoAvailable: Boolean,
+)
@@ -0,0 +1,6 @@
+package de.nowchess.api.dto
+
+final case class GameStateEventDto(`type`: String, state: GameStateDto)
+
+object GameStateEventDto:
+  def apply(state: GameStateDto): GameStateEventDto = GameStateEventDto("gameState", state)
@@ -0,0 +1,7 @@
+package de.nowchess.api.dto
+
+final case class ImportFenRequestDto(
+    fen: String,
+    white: Option[PlayerInfoDto],
+    black: Option[PlayerInfoDto],
+)
@@ -0,0 +1,3 @@
+package de.nowchess.api.dto
+
+final case class ImportPgnRequestDto(pgn: String)
@@ -0,0 +1,9 @@
+package de.nowchess.api.dto
+
+final case class LegalMoveDto(
+    from: String,
+    to: String,
+    uci: String,
+    moveType: String,
+    promotion: Option[String],
+)
@@ -0,0 +1,3 @@
+package de.nowchess.api.dto
+
+final case class LegalMovesResponseDto(moves: List[LegalMoveDto])
@@ -0,0 +1,3 @@
+package de.nowchess.api.dto
+
+final case class OkResponseDto(ok: Boolean = true)
@@ -0,0 +1,3 @@
+package de.nowchess.api.dto
+
+final case class PlayerInfoDto(id: String, displayName: String)
@@ -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=9
 PATCH=0
@@ -1,10 +1,6 @@
-import org.gradle.api.file.DuplicatesStrategy
-import org.gradle.jvm.tasks.Jar
-
 plugins {
    id("scala")
    id("org.scoverage")
-    application
 }

 group = "de.nowchess"
@@ -12,6 +8,8 @@ version = "1.0-SNAPSHOT"

@Suppress("UNCHECKED_CAST")
 val versions = rootProject.extra["VERSIONS"] as Map<String, String>
+@Suppress("UNCHECKED_CAST")
+val scoverageExcluded = rootProject.extra["SCOVERAGE_EXCLUDED"] as List<String>

 repositories {
    mavenCentral()
@@ -23,33 +21,23 @@ scala {

 scoverage {
    scoverageVersion.set(versions["SCOVERAGE"]!!)
-    excludedPackages.set(listOf(
-        "de.nowchess.ui.gui",
-        "de.nowchess.ui.terminal",
-        "de.nowchess.ui.Main",
-    ))
-}
-
-application {
-    mainClass.set("de.nowchess.ui.Main")
+    excludedPackages.set(
+        listOf(
+            "de\\.nowchess\\.bot\\.bots\\.NNUEBot",
+            "de\\.nowchess\\.bot\\.bots\\.nnue\\..*",
+            "de\\.nowchess\\.bot\\.util\\.PolyglotBook",
+        )
+    )
+    excludedFiles.set(scoverageExcluded)
 }

 tasks.withType<ScalaCompile> {
    scalaCompileOptions.additionalParameters = listOf("-encoding", "UTF-8")
 }

-tasks.named<JavaExec>("run") {
-    jvmArgs("-Dfile.encoding=UTF-8", "-Dstdout.encoding=UTF-8", "-Dstderr.encoding=UTF-8")
-    standardInput = System.`in`
-}
-
-tasks.named<Jar>("jar") {
-    duplicatesStrategy = DuplicatesStrategy.EXCLUDE
-}
-
 dependencies {

-    implementation("org.scala-lang:scala3-compiler_3") {
+    compileOnly("org.scala-lang:scala3-compiler_3") {
        version {
            strictly(versions["SCALA3"]!!)
        }
@@ -60,27 +48,10 @@ dependencies {
        }
    }

-    implementation(project(":modules:core"))
-    implementation(project(":modules:rule"))
    implementation(project(":modules:api"))
    implementation(project(":modules:io"))
-
-    // ScalaFX dependencies
-    implementation("org.scalafx:scalafx_3:${versions["SCALAFX"]!!}")
-    
-    // JavaFX dependencies for the current platform
-    val javaFXVersion = versions["JAVAFX"]!!
-    val osName = System.getProperty("os.name").lowercase()
-    val platform = when {
-        osName.contains("win") -> "win"
-        osName.contains("mac") -> "mac"
-        osName.contains("linux") -> "linux"
-        else -> "linux"
-    }
-    
-    listOf("base", "controls", "graphics", "media").forEach { module ->
-        implementation("org.openjfx:javafx-$module:$javaFXVersion:$platform")
-    }
+    implementation(project(":modules:rule"))
+    implementation("com.microsoft.onnxruntime:onnxruntime:${versions["ONNXRUNTIME"]!!}")

    testImplementation(platform("org.junit:junit-bom:${versions["JUNIT_BOM"]!!}"))
    testImplementation("org.junit.jupiter:junit-jupiter")
@@ -102,3 +73,7 @@ tasks.test {
 tasks.reportScoverage {
    dependsOn(tasks.test)
 }
+
+tasks.jar {
+    duplicatesStrategy = DuplicatesStrategy.EXCLUDE
+}
@@ -0,0 +1,22 @@
+# Data and weights are local artifacts, not committed
+data/
+
+# Python
+__pycache__/
+*.py[cod]
+*$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)"
+}
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
Janis	b5a2966ada	feat: NCS-53 changed IO to MicroService for easier scaling (#37 ) Reviewed-on: #37 Reviewed-by: Shahd Lala <shosho996@blackhole.local>	2026-04-21 15:38:58 +02:00
TeamCity	74a4fce0ca	ci: bump version with Build-44	2026-04-21 11:13:30 +00:00
Janis	8fc97bde02	refactor: align JSON string formatting in JsonParserTest	2026-04-21 13:02:18 +02:00
shosho996	2d75b2e80e	test: NCS-45 IO Test reduction (#32 ) Co-authored-by: shahdlala66 <shahd.lala66@gmail.com> Reviewed-on: #32 Co-authored-by: Shahd Lala <shosho996@blackhole.local> Co-committed-by: Shahd Lala <shosho996@blackhole.local>	2026-04-21 12:39:19 +02:00
Janis	f088c4e9ff	feat: NCS-37 Quarkus integration (#35 ) Reviewed-on: #35 Reviewed-by: Leon Hermann <lq@blackhole.local>	2026-04-21 12:35:20 +02:00
TeamCity	8a1cf909d4	ci: bump version with Build-43	2026-04-19 20:53:56 +00:00
Janis	33e785d22a	feat: NCS-40 Rework Draw System (#34 ) Reviewed-on: #34 Reviewed-by: Shahd Lala <shosho996@blackhole.local> Co-authored-by: Janis <janis.e.20@gmx.de> Co-committed-by: Janis <janis.e.20@gmx.de>	2026-04-19 22:44:48 +02:00
TeamCity	d16cec176b	ci: bump version with Build-42	2026-04-19 14:01:11 +00:00
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