$ git checkout autoresearch/engineering/lead-scoring Switched to branch 'autoresearch/engineering/lead-scoring' $ cat .autoresearch/engineering/lead-scoring/config.cfg target_file = src/scoring/engine.py metric = p50_ms direction = lower_is_better baseline = 520 budget = 50 iterations test_cmd = pytest tests/bench/test_scoring.py --benchmark-only -q $ cat .autoresearch/engineering/lead-scoring/program.md | head -8 ## Estrategia aprendida (iter 1–31) Dirección prometedora: async I/O + numpy vectorization Evitar: modificaciones al modelo ML (alto riesgo, ×3 crashes) Siguiente: batch async pipeline con Redis pre-cache $ python setup_experiment.py --status engineering/lead-scoring ✓ Resuming: engineering/lead-scoring Target: src/scoring/engine.py Metric: p50_ms (lower is better) Experiments: 31 total — 18 kept, 10 discarded, 3 crashed Best: 187ms (−64% from baseline of 520ms) Last iter: #31 "numpy vectorize feature matrix" → KEEP (201ms) Recent patterns: ✓ I/O async changes: 7 kept, 1 discarded (dirección principal) ✓ Numpy vectorization: 5 kept, 2 discarded (combinable con async) ⚠ ML model changes: 0 kept, 2 discarded, 3 crashed (evitar) ✓ Redis caching: 4 kept, 1 discarded (prometedor, no saturado) ○ Batch pipeline: 2 kept, 0 discarded (poco explorado, PRIORIDAD) ✓ Historial cargado — listo para continuar desde iter #32
| Categoría de cambio | Keep | Disc. | Crash | Señal | Prioridad |
|---|---|---|---|---|---|
| async I/O pipeline | 7 | 1 | — | ●●●●● | ALTA |
| numpy vectorization | 5 | 2 | — | ●●●●○ | ALTA |
| Redis pre-cache | 4 | 1 | — | ●●●●○ | PRÓXIMO |
| batch processing | 2 | 0 | — | ●●●○○ | EXPLOR. |
| query optimization | 2 | 3 | — | ●●○○○ | MEDIA |
| ML model tuning | 0 | 2 | 3 | ●○○○○ | EVITAR |