Ignore static/, logs/, data/rag_chroma/, api/, image_cache/, .claude/,
precon-decks/, and dependency-reduced-pom.xml — all runtime/build
artifacts that were polluting git status. Also stage the one-off
patch scripts (fix_paths.py, patch_eval.py, patch_phase1.py,
forge_evaluator_FIXED.py) and training_manifest.json so they're
tracked alongside the code they support.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Add user-decks/ directory and USER_DECKS_DIR config for exported decks
- Update export-to-sim to write to user-decks/ with static mount in lab_api
- Replace import/paste UI with API-backed "My Decks" dropdown in LAN client
and AI Lab, fetching from GET /api/lab/decks with source grouping
- Fix GET /api/lab/decks to JOIN deck_cards for totalCards count
- Fix deckgenerator: add missing DOM elements (dg-strategy-bar, dg-stats-bar,
dg-card-groups) inside #dg-results, null guards on render functions,
fix setLoading always hiding results
- Replace gpt-oss:20b with claude-opus-4-20250514 across all files
- Add user-decks/ to _BUILTIN_DECK_DIRS in deck_service.py
- Enrich missing type_line from Forge card cache in DB deck path
- Collapse import bar behind <details> toggle in AI Lab
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Issue #1 — Winner's life total now captured:
- Parse verbose Forge log for life-total-change events
- Track last-known life per seat via 3 regex patterns
- Winner's final life set from log data (not left at default 40)
- Losers who died to mill/poison retain their actual life total
Issue #2 — Win condition classification formalized:
- New WinCondition enum (schema/WinCondition.java) with 8 categories
- Centralized Forge loss-reason-string → enum mapping (regex-based)
- UNKNOWN fallback with logging for unrecognized loss reasons
- All classification now goes through WinCondition.classify()
Issue #3 — JVM startup overhead reduced:
- Added -XX:+UseSerialGC (lighter GC for batch processes)
- Added -XX:TieredStopAtLevel=1 (faster JIT, less compilation)
- ForgeWorker stub class for future persistent batch mode
- Estimated reduction: ~2-3s → ~1-1.5s per game startup
Issue #4 — Single-threaded throughput optimization:
- New CLI flags: --ai-simplified, --ai-think-time, --benchmark
- JVM flags passed through to Forge subprocess
- Benchmark mode: run 1 game before batch, report wall/Forge/overhead times
- API support: aiSimplified, aiThinkTimeMs in StartRequest
Issue #5 — Threading, progress, and backpressure:
- --threads 0 or -1 auto-detects optimal count from CPU topology
- detectOptimalThreads() estimates physical cores (excludes HT)
- ProgressCallback interface for real-time sims/sec reporting
- Per-game progress emitted as [PROGRESS] lines, parsed by lab_api.py
- --max-queue flag for graceful backpressure (limits concurrent JVMs)
- Final throughput summary printed at batch completion
- StatusResponse now includes simsPerSec field
Also: .gitignore restored, lab_api.py updated with all new API fields
