chore: scaffold oO monorepo with architecture, roadmap, and module stubs

docs/architecture/overview.md

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+# Architecture overview
+
+## Guiding constraints
+
+- The **recommendation decision** is the hot path. Every architectural choice should shorten the distance between a new signal and a better tip.
+- Services are small and independently deployable, but we do **not** multiply services for its own sake. Split by team-of-ownership and by data lifecycle.
+- Python for ML, TypeScript for applications, shared contracts regenerated from a single source of truth.
+
+## Services
+
+| Service | Language | Responsibility | Owns data |
+|---|---|---|---|
+| `gateway` | TS (Node) | BFF for web/mobile; auth-checking; request fan-out | — |
+| `auth` | TS | OAuth (Google, Apple), sessions, token issuance | identities, sessions |
+| `profile` | TS | user profile, preferences, consents | profiles |
+| `integrations` | TS | third-party connectors, token vault, signal fetch | credentials, cursors |
+| `events` | TS | event-bus ingress, normalization, durable log | signal store |
+| `recommender` | TS | orchestration: candidates → policy → tip; feedback sink | tip history |
+| `ml/serving` | Python | online scoring for policies/models | — (stateless) |
+| `ml/pipelines` | Python | batch feature + training pipelines | feature store, models |
+| `notifier` | TS | push/email delivery, quiet hours, dedupe | delivery log |
+
+## Data boundaries
+
+Each service owns its schema; no cross-service DB access. When `recommender` needs profile data, it calls `profile` (read model), not its DB.
+
+## Event flow
+
+```
+connector (integrations) ──emit──▶ events ──▶ feature pipelines (ml)
+                                     │
+                                     └──▶ recommender (context assembly)
+```
+
+User reactions (done / snooze / dismiss) are events too. They close the loop as rewards for bandit/RL policies.
+
+## Why these choices
+
+- **NATS JetStream** over Kafka for Phase 1: lighter, single-binary, fits the "one VM" deployment. Swap to Kafka in Phase 4.
+- **Postgres** everywhere for OLTP. Per-service schemas, not per-service instances in dev.
+- **FastAPI + Pydantic** for ML serving — fast, typed, swappable runtime (ONNX, Triton) behind it.
+- **Feast** for feature store when we get there; homegrown adapter until then (Phase 1 seam).
+- **MLflow** for model registry; artifacts in MinIO/S3.
+- **Auth.js or Ory** for identity — we will not write crypto.