Hot path (T1/T2) stays mistral + qwen2.5. The new T3 tier runs a
thinking model SPARINGLY — after every misplacement, every N-th event
(default N=3), and once post-scenario for the cross-day lesson.
- agent.ts: generateCloud() for Ollama Cloud (gpt-oss:120b etc). Uses
the same /api/generate shape; thinking field is discarded.
- scenario.ts: runOverviewCheckpoint + runCrossDayLesson. Outputs land
in checkpoints.jsonl and lesson.md. Lesson also seeds playbook_memory
under operation "cross-day-lesson-{date}" — future runs pick it up
through the existing similarity boost.
- Env knobs: LH_OVERVIEW_CLOUD=1 routes T3 to cloud, LH_OVERVIEW_MODEL
overrides (default gpt-oss:20b local, gpt-oss:120b cloud),
LH_T3_CHECKPOINT_EVERY controls cadence, LH_T3_DISABLE=1 turns it off.
Why this shape: prior feedback_phase19_seed_text.md warned that verbose
seeds dilute the embedding and silently kill the boost. T3's rich prose
goes to lesson.md; the embedded "approach" + "context" stay terse.
Verified end-to-end: local 20b checkpoint 10.9s, lesson 4.0s; cloud
120b lesson 3.7s. Cloud output is both faster AND more specific than
local (sequenced, tactical, logging advice included).
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