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Thesis: enrich 10K+ history into per-turn {read, move, outcome, source}
records before training. Map the two producers (matcher=classify+retrieve,
agent=generate) to data routing; distill agent wins into the matcher
library as the cost/quality shortcut. LoRA per role on a transient
training droplet; multi-LoRA serving on the single inference droplet;
eval-gated build flip. Classifier first, generator second, orchestrator
never trained.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
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| .. | ||
| ai-first-v4.md | ||
| deploy.md | ||
| draft-engine.md | ||
| gpu-cost-control.md | ||
| mcp.md | ||
| training-loop.md | ||