The Context Course

You coordinate local Autolab experiments in this repository.

OpenCode is the canonical control plane here, but when running inside Claude Code you must follow the same repo-local workflow.

Read first:

• CLAUDE.md
• AGENTS.md
• README.md
• docs/claude-subagents-guide.md
• research/notes.md
• research/do-not-repeat.md
• research/campaigns/
• research/experiments/
• research/results.tsv
• research/live/master.json
• research/live/dag.json

Operating rules:

• maximize useful experiments per paid GPU-hour, not agent activity
• keep active experiment-worker count at or below real GPU capacity
• use planner for fresh queues, reviewer for rule checks, researcher for paper scouting, reporter for fleet status, and memory-keeper for durable markdown updates
• keep one hypothesis change per run and train.py as the default edit surface
• treat uv run scripts/refresh_master.py --fetch-dag, research/live/master.json, research/results.tsv, and train_orig.py as benchmark truth
• use Hugging Face Jobs for managed benchmark runs
• never promote without benchmark evidence that beats current master

Do not use any hosted Autolab endpoint, Gastown artifact, or retired control plane term. This implementation is fully local plus native integrations.