# oO — Project Instructions

## What this is

**oO** is a recommendation system for personal tips. It collects signals across a user's life (tasks, habits, calendar, mood, context) to build a rich profile and deliver **one** perfectly-timed tip — an advice or a todo — that feels like magic.

The magic is the product. Precision + timing + minimalism. The UI shows a single black page with one tip. The complexity lives behind it.

## Prime directives

1. **Modular, service-oriented from day one.** Even the prototype. We will scale to mobile (iOS/Android), many integrations, multi-tenant ML. Shortcuts that bake in a monolith are not acceptable.
2. **Recommendation engine is the core.** Every other service feeds it or renders its output. Design schemas, event contracts, and APIs with that in mind.
3. **Python owns ML.** Everything training, features, serving for models is Python (FastAPI + PyTorch/scikit + MLflow/feast). Application services are TypeScript (Node, Next.js) unless there's a reason.
4. **OAuth-first for identity and integrations.** Never ask users for passwords or raw API keys when a delegated-auth flow exists. Store provider tokens encrypted, refresh transparently.
5. **Feel-of-magic over feature count.** When in doubt, ship fewer things, polished.

## Architecture (high level)

```
apps/              user-facing clients
  web/             Next.js PWA — the first shipped client
  mobile-ios/      Swift/SwiftUI (Phase 3)
  mobile-android/  Kotlin/Compose (Phase 3)

services/          backend microservices (each independently deployable)
  gateway/         API gateway + BFF (GraphQL or tRPC)
  auth/            OAuth (Google, Apple, ...), sessions, JWT issuance
  profile/         user profile, preferences, consents
  integrations/    third-party connectors (Todoist first); token vault
  recommender/     Python; serves the "one best tip" decision
  events/          event bus ingress (Kafka/NATS) + signal store
  notifier/        push/email/web delivery of tips

packages/          shared libraries
  shared-types/    OpenAPI/proto-generated types
  sdk-js/          client SDK used by web + mobile webviews
  ui/              shared React components + design tokens

ml/                Python MLOps
  pipelines/       training / batch feature pipelines (Airflow/Prefect)
  features/        feature definitions (Feast-style)
  registry/        model registry (MLflow) integration
  experiments/     A/B testing framework + bandit policies
  serving/         online inference service (FastAPI)
  notebooks/       research only — not production

infra/             docker-compose, k8s manifests, terraform, CI
docs/              architecture notes, ADRs, API specs
```

## Contracts between services

- **Events** (Kafka/NATS) — source of truth for user signals. All integrations emit normalized events; the recommender reads them.
- **HTTP/gRPC** — synchronous request/response (gateway → services).
- **Shared schemas** live in `packages/shared-types`; generated from a single OpenAPI / proto source. Do not redefine types per service.

## Conventions

- Every service ships a `README.md`, a `Dockerfile`, and a `/health` endpoint.
- One PR = one concern. Commits follow conventional-commit prefixes (`feat:`, `fix:`, `chore:`, `docs:`, `refactor:`).
- ADRs go in `docs/adr/NNNN-title.md` for any decision that constrains future work.
- No secrets in repo. Local dev via `.env.local` (gitignored), prod via the server's secret store (Vaultwarden now; k8s secrets later).

## Definition of done (per feature)

1. Code + tests merged.
2. Service's `README.md` updated.
3. If it changes a contract → `shared-types` regenerated + consumers updated.
4. If it changes architecture → ADR added.
5. Deployable via `docker compose up` locally.

## Current phase

**Phase 0 — Prototype.** See `README.md` for the phase roadmap and `docs/architecture/` for diagrams. Work is tracked as Gitea milestones + issues on `alvis/oO`.

## What NOT to do

- Don't copy Todoist's data into our DB. Store the OAuth token; fetch on demand.
- Don't implement auth by hand. Use a library (NextAuth / Auth.js, Ory, or Clerk-compatible). We will self-host.
- Don't hardwire a recommender. The "random todo" v0 must live behind the same interface the real ML model will implement (`POST /recommend` → `{tip}`). Swap internals, keep contract.
- Don't build an admin UI before the user-facing black page is polished.