GPU Scheduler (gpu.sethpc.xyz):
- Live dashboard with 4 GPUs, training monitor, loss sparklines
- Preset-based job scheduler with 3 triggers (time, finish_training, cost)
- Model selection per GPU, pipeline configuration
- Tool self-play and training pipeline types
- Behind Google OAuth, live-refresh without page reload
Tool Architecture (14 tools):
- 3 new tools: world.nearby_entities, memory.read, memory.write
- 7 script.* tools: write, validate, execute, read, list, delete, schedule
- ScriptManager: full mcfunction datapack CRUD with RCON validation
- Training data: 1,430 tool examples (up from 1,159)
Plugin Deployment (paper-ai-25567):
- WorldGuard 7.0.12, CoreProtect CE 23.1, EssentialsX 2.21.2, Vault 1.7.3
- Fresh greenfield world reset
- 104 RCON-validated plugin training examples
Event Dispatcher:
- Watches server log for deaths, joins, advancements, PvP kills
- Configurable trigger probability and cooldowns per event type
- Deployed to dev server, fires god_system prompts on events
- 21 event-response training examples
Training Infrastructure:
- train_lora.py: --save-steps 50, --resume from checkpoint
- run_training.sh: stops Ollama, activates conda, restarts after
- Passwordless sudo for ollama services on steel141
- Dev server added to MCSManager with autoStart
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Root cause: self-play opened/closed a new TCP socket for every RCON command
(hundreds/minute). Paper's RCON listener creates a thread per connection,
overwhelming the server until it stopped.
Fix: PersistentRCON class maintains a single connection per server with
auto-reconnect. Thread-safe via lock. Connection pool keyed by host:port.
Applied to:
- mc_aigod_paper.py (prod paper-ai + dev)
- mc_aigod.py (shrink-world)
- self_play.py (training data generation)
- persistent_rcon.py (shared module)
Before: ~100+ RCON connections/minute → server crash
After: 3 persistent connections total → stable
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- IDEA.md: project scope (Minecraft ops AI assistant via qwen3-coder LoRA/SFT)
- PLAN.md: complete roadmap with prior art analysis, architecture, phased plan, dev server docs
- data/schema.json: training example JSON Schema with negative_output support
- data/processed/seed_dataset.jsonl: 31 validated examples from repair code, prayer logs, session history
- data/validate_dataset.py: schema validator with summary statistics
- ingame/: Mineflayer bot framework (test_connect, spawn_bots, aware_bots with full event logging)
- Directory structure for knowledge/, eval/, training/, agent/ (Phase 1.3+ work)
Mortdecai
Seth