Backend:
- POST /api/coach/chat: Multi-turn coaching chat with SSE streaming
- POST /api/coach/apply: Apply suggestions to deck (cut/add cards)
- GET /api/coach/cards-like: Embedding similarity search with ownership/price
- GET /api/coach/decks: Enhanced to show all decks with report status
- Goals config (power level, strategy, budget, focus areas) wired to prompts
- Session history with full diff tracking (cuts/adds/hints)
Frontend (complete CoachPage rewrite):
- Live multi-turn chat with streaming LLM responses
- Apply suggestions UI: checkbox cuts/adds, one-click apply to deck
- Goals config panel (power level slider, strategy chips, budget, focus areas)
- Session results view with summary, tips, mana advice, cuts, adds
- Session history browser with session drill-down
- Cards Like This search (embedding-powered card discovery)
- Price-aware card suggestions with collection ownership badges
- Responsive dark theme, tab navigation
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
KoalaTrapLord