oO/README.md

# oO

> One tip. Right now. Feels like magic.

oO learns who you are from the apps you already use and surfaces **one** perfectly-timed suggestion — an advice or a todo — on a black page. No feed. No dashboard. One tip.

---

## Why

Everyone has too many tasks, too many apps, too much noise. What people actually need is a single, well-chosen nudge at the right moment. oO is that nudge, powered by a recommendation engine that gets smarter the more of your life it sees.

## Product principles

1. **One thing at a time.** The UI is a black page with one tip. That's the product.
2. **We don't own your data, we understand it.** Connect your apps; we read what we need, when we need it.
3. **Magic requires craft.** Precision, timing, and restraint matter more than features.
4. **Private by default.** Tokens are encrypted, models are per-user, deletion is one click.

## Prototype scope (Phase 0)

Three pages. That's it.

| Page | What it does |
|------|--------------|
| **Sign in** | Google / Apple OAuth. No passwords. |
| **Connect** | A list of integrations. Tap "Todoist" → OAuth flow → token stored. |
| **Tip** | Black page. One tip. Tap to dismiss / done / snooze. |

Under the hood the "pick a tip" call already routes through a `recommender` service with a pluggable policy — so v0 is literally "random Todoist task" but every other version slots into the same contract.

---

## Architecture at a glance

```
 ┌──────────┐   OAuth   ┌────────────┐
 │  Web /   │──────────▶│   auth     │
 │  Mobile  │           └─────┬──────┘
 │  client  │                 │ JWT
 │          │   REST/GraphQL  ▼
 │          │────────▶┌───────────────┐
 └──────────┘         │   gateway     │──┬──▶ profile
                      └───────┬───────┘  ├──▶ integrations ──▶ Todoist / Google / ...
                              │          └──▶ recommender ──▶ ml/serving (Python)
                              ▼
                      ┌───────────────┐
                      │    events     │ ◀── integrations emit normalized events
                      │  (Kafka/NATS) │ ──▶ ml/pipelines (features, training)
                      └───────────────┘
```

More detail in [`docs/architecture/`](docs/architecture/) and decisions in [`docs/adr/`](docs/adr/).

## Monorepo layout

See [`CLAUDE.md`](CLAUDE.md) for the full tree and conventions.

```
apps/        web, ios, android
services/    gateway, auth, profile, integrations, recommender, events, notifier
packages/    shared-types, sdk-js, ui
ml/          pipelines, features, registry, experiments, serving
infra/       docker, k8s, terraform, ci
docs/        architecture, adr, api
```

---

## AI stack

oO is AI-native. Domain-specialized agents pre-compute snippets describing the user's state from one angle each; an orchestrator LLM reasons over the assembled snippets and produces one tip (ADR-0013). The orchestrator iterates a registry, not a hardcoded list (ADR-0014) — adding an agent is a manifest change, nothing else.

### Three-tier layout

| Tier | Service | Purpose | Where |
|------|---------|---------|-------|
| Inference | **Ollama** | Local LLM + embedding; no data leaves the host | `localhost:11434` |
| Routing | **LiteLLM** | Unified OpenAI-compatible API; model aliases; cloud fallback | `llm.alogins.net` (Agap shared) |
| Testing | **OpenWebUI** | Prompt iteration, model comparison, manual evals | `ai.alogins.net` (Agap shared) |

### Tip generation pipeline (ADR-0013, M2)

```
User signals          Pre-compute agents (every 15 min)
(tasks, calendar,  ──▶ ml/agents/{overdue-task, momentum,        ──▶  agent_outputs
 patterns, time)        time-of-day, recent-patterns,                 (per-agent TTL)
                        focus-area, ...}
                                                                            │
                              Eligibility filter: required consents +       │
                              active context + per-user prefs (ADR-0014) ◀──┘
                                                ▼
                                  Orchestrator prompt (`v4-orchestrator`)
                                  = global prefs + active context + snippets
                                                ▼
                                    LiteLLM ──▶ Ollama (local) / cloud fallback
                                                ▼
                                         Tip shown to user
                                                ▼
                              User reaction (done / snooze / dismiss + dwell)
                                                ▼
                              Logged to tip_feedback for observability
                              (no online ML reward loop — see ADR-0013)
```

**Why LiteLLM as gateway:**  All LLM calls use a single `LITELLM_URL` env var. Swapping from qwen2.5 to llama3.2, or routing a fraction to Claude for A/B, is a config change in LiteLLM — zero code change in oO. The model name in `tip_scores` tells you exactly which model produced each tip.

**Why Ollama first:**  Tips contain personal context. Local inference means no user data leaves the host for the inference path. Cloud models (Anthropic, OpenAI) are opt-in fallbacks for evaluation and simulation only, gated behind `ANTHROPIC_API_KEY`.

### Models (planned; routes through LiteLLM)

| Alias | Model | Task |
|-------|-------|------|
| `tip-generator` | qwen2.5:1.5b (default) | Generate typed tip candidates from user context; local-first via Ollama |
| `embedder` | nomic-embed-text | Task clustering, semantic similarity for dedup; local via Ollama |
| `judge` | claude-haiku-4-5 (cloud, eval-only) | Offline sim judge; rates tip quality for A/B (requires `ANTHROPIC_API_KEY`) |

All model calls route through **LiteLLM** at `llm.alogins.net` (or `LITELLM_URL` env var) using model aliases. This decouples tip generation from model selection — swap the backend model in LiteLLM config without code changes. See ADR-0008.

---

## Roadmap

### Phase 0 — Walking skeleton  *(M0)* ✓ shipped
Goal: a single user signs in with Google, connects Todoist, and sees one random Todoist task on a black page. Deletion works.
- [x] Monorepo scaffold, docker-compose dev env
- [x] `auth` — Google OAuth2/PKCE via openid-client v6; session cookie; Next.js middleware guard
- [x] `integrations/todoist` — OAuth2 flow, token stored in DB, disconnect supported
- [x] `recommender` with `RandomPolicy`; stable `POST /recommend` contract; 30s task cache
- [x] `apps/web` — sign-in, connect, tip pages; PWA manifest + icons
- [x] Feedback: `done / snooze / dismiss`; reward inferred from dwell-time (`inferReward`); marks task complete in Todoist
- [x] Deploy modular monolith to Agap VM via Caddy at `o.alogins.net`
- [x] ToS + Privacy Policy pages (`/legal/terms`, `/legal/privacy`); implicit consent on sign-in
- [x] Account deletion: revokes tokens, purges data, soft-deletes profile; button on /connect
- [x] Metrics baseline: `tip_views` table (tip served) + `tip_feedback` (reactions) — activation + reaction rate queryable

### Phase 1 — Real signal + in-the-moment delivery  *(M1)* ✓ shipped
Goal: tips are picked, not drawn from a hat — and they arrive at the right moment on the web.
- [x] Event bus scaffold: typed in-process EventEmitter with 500-event ring buffer; subjects match future NATS JetStream — swap is mechanical
- [x] Todoist sync emits `signals.task.synced`; tip served/feedback emit `signals.tip.*`
- [x] Features extracted per task: `is_overdue`, `task_age_days`, `priority`; context: `hour_of_day`, `day_of_week`
- [x] **ε-greedy v1** (d=7, ε=0.10, day-of-week sin/cos features); per-user state persisted to disk
- [x] **ε-greedy v2** (d=12, profile features: completion rate, dismiss rate, dwell, preferred hour, tip volume) in shadow; promoted to active policy (ADR-0012)
- [x] `RemotePolicy` in recommender: calls ml/serving, falls back to RandomPolicy on timeout/error; logs explainability to `tip_scores`
- [x] Feedback loop: dwell-time inferred reward (`inferReward`) → online model update; `done` in 15 s–2 min = +1.0 (magic zone)
- [x] Offline simulation framework (`ml/experiments/sim`): rule/LLM/claude-code judges, two-policy comparison, results persisted to `sim_runs` + `sim_events`
- [x] **Web Push** (VAPID): SW, subscribe/unsubscribe API, "notify me" button on tip page
- [x] Shadow-policy registry: run N shadow policies per request, log picks without serving them (#56)
- [x] NATS JetStream bridge — durable `signals.>` and `feedback.>` streams; in-process bus stays the source of truth, every publish bridges out (#21, shipped)
- [x] Per-user profile features (completion rate, dismiss rate, dwell, preferred hour, tip volume) — event-driven, JIT invalidation (#81)
- [ ] Quiet-hours + dedupe for push delivery
- [ ] Delayed rewards: tasks completed directly in Todoist (requires webhook from Todoist)
- [ ] Apple OAuth (deferred to M3)

#### M1 add-on — Admin & ML Ops Console  *(fully shipped)*

oO is ML-heavy. Without a cockpit, every model change ships blind. This console is the team's single pane for users, signals, features, models, experiments, and tip outcomes — with the ability to *act* on them (revoke a token, replay an event, promote a model, reset a bandit).

**Framework pick — `apps/admin` on Next.js 15 + Tremor + shadcn/ui.**  Analytics-first UI for an analytics-first product, stays on our existing TS/React/Tailwind stack, reuses `packages/shared-types`, `sdk-js`, and the Auth.js session. Specialized ML tooling (MLflow) runs as a **separate external service** linked from the admin shell; Grafana panels are embedded.

| Layer | Tool | Why |
|-------|------|-----|
| App shell | **Next.js 15** (new `apps/admin`) | Same stack as `apps/web`; reuses auth, types, SDK |
| Dashboards / charts | **[Tremor](https://tremor.so)** | Analytics-first React + Tailwind — KPI cards, time-series, categorical, heatmaps |
| CRUD primitives | **[shadcn/ui](https://ui.shadcn.com)** | Copy-paste Radix components; forms, dialogs, command palette |
| Heavy grids | **[TanStack Table v8](https://tanstack.com/table)** | Sortable / paginated / virtualized tables (events, users, tips) |
| Extra charts | **[Recharts](https://recharts.org)** / **[visx](https://airbnb.io/visx)** | Fallbacks where Tremor falls short (e.g. force graphs, Sankey) |
| Model registry / experiments | **[MLflow](https://mlflow.org)** *(external — `o.alogins.net/mlflow`)* | Experiment tracking, artifact browser, model registry; own basic-auth |
| Infra metrics | **[Grafana](https://grafana.com)** *(embedded panels)* | One ops source of truth |
| Ad-hoc analysis | **[Marimo](https://marimo.io)** reactive notebooks | Python-native for the ML side; launch-out link |
| AuthZ | `profile.role='admin'` + Next.js middleware | Reuses existing session; no new auth surface |

**Rejected alternatives (so we don't re-litigate):**
- *Retool / AppSmith* — low-code speed, but admin logic leaves our repo; weak analytics affordances for an analytics product
- *Streamlit / Gradio / Dash* — Python-first; thin RBAC and routing; splits our frontend stack in two
- *React-admin / Refine.dev* — strong CRUD scaffolding, but analytics/ML views feel bolted on; we'd rebuild Tremor-style dashboards ourselves
- *Superset / Metabase as the admin surface* — excellent for BI, poor for operational **writes** (revoke, replay, promote). Plan: **adopt Superset in M4** for BI alongside batch pipelines; ship a read-only SQL widget inside admin for now

**Build sequence:**
1. [x] **ADR-0006** — record the framework choice + "embed, don't rebuild" rule for MLflow/Grafana
2. [x] **Scaffold** — `apps/admin` with Next.js 15, Tailwind, Tremor; deploy behind Caddy at `admin.o.alogins.net`
3. [x] **RBAC** — `role` column on `users`; admin-only Next.js middleware; seed first admin via `ADMIN_SEED_EMAIL` env; `admin_actions` audit-log table
4. [x] **Overview dashboard** — DAU/WAU KPI cards, tips served, reaction breakdown, activation funnel
5. [x] **User explorer** — list + detail page: identity, consents, integrations, last tip, reward history; revoke-integration + reset-bandit + rebuild-profile actions
6. [x] **Event stream viewer** — live tail of `signals.*` with filters by subject/user/time; same UI when the bus swaps to NATS
7. [x] **Features page** — features sent to `ml/serving` per scoring call; per-user profile features with freshness; diff across time
8. [x] **Tips page** — tips served, scored, feedback reactions with policy/model breakdown
9. [x] **Reward analytics** — reaction distribution over time; per-policy / per-model / per-prompt-version compare; slice by `hour_of_day`, `priority`, cohort
10. [x] **Data quality widget** — missing-feature rate, stale-token rate, daily completeness heatmap; per-feature freshness SLA status
11. [x] **Ops actions** — revoke token (Users page), rebuild profile, reset bandit, enable/disable shadow policies; every action audit-logged
12. [x] **Health rollup** — `/admin/health` surfaces api, ml/serving, SQLite, event-bus, MLflow; auto-refreshes every 15s
13. [x] **Read-only SQL runner** — SELECT-only runner against SQLite + saved queries (sunsets to Superset in M4)
14. [x] **Offline simulation runner** — launch `ml/experiments/sim` from admin UI; track sim runs, judge, policy comparison
15. [x] **Token-based admin auth** — `POST /api/auth/token` for Playwright/CI; `ADMIN_TOKEN` env var (#105)
16. [x] **Docs pages** — admin documentation and runbooks inline

### Phase 2 — AI tips + multi-source signals  *(M2)* ✓ shipped
Goal: tips are AI-generated from user context, not just raw Todoist tasks. Multiple signal sources feed a generalized pipeline. Research-intensive milestone.

**Architectural shift (mid-M2):** the bandit-ranks-LLM-candidates design from earlier in M2 was replaced with a multi-agent pipeline (ADR-0013): pre-compute agents emit prompt snippets, an orchestrator LLM produces the tip directly. ADR-0014 layers a unified Profile + agent registry + auto-inference framework on top so the system generalizes cleanly to N agents.

**Multi-agent recommendation (ADR-0013, shipped):**
- [x] `agent_outputs` table + per-agent TTL caching
- [x] Five initial agents: `overdue-task`, `momentum`, `time-of-day`, `recent-patterns`, `focus-area`
- [x] Agent pre-compute scheduler
- [x] Orchestrator cutover — recommender calls `ml/serving` with snippet list, no bandit scoring
- [x] Bandit endpoints + shadow policy machinery removed

**Unified Profile + agent registry (ADR-0014, shipped):**
- [x] Unified Profile model: prefs, contexts, consents + manifest plumbing + orchestrator cutover (#30)
- [x] Shared context-inference framework (#111)
- [x] Per-agent auto-inference: `time-of-day` (#112), `focus-area` (#113), `momentum` (#114), `overdue-task` (#115), `recent-patterns` (#116)

**AI infrastructure (unblock everything else):**
- [x] `ai` compose profile — Ollama + LiteLLM for local dev; env vars `OLLAMA_URL` / `LITELLM_URL` (#86)
- [x] AI gateway — wire `ml/serving` to LiteLLM; model aliases `tip-generator` + `embedder` (#87)

**AI tip generation pipeline:**
- [x] Context assembler — user signals + feature store → structured prompt context (`ml/features/context.py`); skeleton implemented
- [x] Tip generator endpoint — `POST /generate` in `ml/serving`; LLM → N typed `TipCandidate` objects (#79)
- [x] `TipCandidate` shared schema — `{content, kind, source, model, prompt_version, confidence}`; update recommender pipeline (#89)
- [x] LLM output validation + retry — JSON schema gate, clarification retry (2×), fallback to hardcoded tips on AI failure (#90)
- [x] Prompt versioning — `prompt_version` + `model` columns in `tip_scores`; content-hash invalidation (#91)
- [x] LLM tip quality dashboard — reaction breakdown by model / prompt_version in `/admin/reward-analytics` (#92)

**Evaluation & model selection:**
- [x] Model benchmark — compare qwen2.5:7b / llama3.2:3b / gemma3:4b via offline sim + LLM judge (#93)
- [x] LLM prompt research — persona design, context injection strategies, few-shot examples (#84, #95)

**Pipeline architecture:**
- [x] Signal source abstraction — `SignalSource` interface for Todoist + extensible design (#78)
- [x] Generalized recommendation pipeline — superseded by ADR-0013; multi-agent orchestrator is the pipeline (#80)
- [x] Feature registry + user profile builder — centralized features, persistent profiles, event-driven invalidation (#81)
- [ ] Tip kind system — task, advice, insight, reminder with kind-aware UI + rewards (#82)

**Policy research:**
- [ ] Next-gen policies — Thompson sampling, neural bandits, hybrid transfer learning (#83)

**Integrations & infra (carried from M1):**
- [ ] Apple OAuth (#7)
- [x] NATS JetStream replacing in-process bus (#21) — adapter ships in `services/api/src/events/nats.ts`; in-proc bus is the producer, JetStream is the durable mirror
- [x] Todoist sync via events (#22) — background scheduler in `services/api/src/signals/scheduler.ts` emits `signals.task.synced` every `TODOIST_SYNC_INTERVAL_MS`; on-demand fetch remains as freshness fallback
- [x] Event schema registry + protobuf CI gate (#54) — buf lint/breaking checks on every PR
- [x] Per-user freshness SLAs for features (#61) — context-feature (JIT) vs profile-feature (batched) spec in ADR-0011; `invalidated_by` mirrored into `ProfileFeature`; CONTEXT_FEATURES in ml/features/context.py
- [x] Embedding-based task clustering — `nomic-embed-text` for semantic dedup + focus-area features (#97)
- [x] Observability (#18) — structured logs via pino, W3C trace IDs, Sentry hooks, trace correlation end-to-end
- [ ] CI skeleton (#3), E2E tests (#20)

**Bugs & UX (fix before new features):**
- [x] TipFeedback type mismatch (#73)
- [x] Todoist token refresh (#74) — OAuth token auto-refresh on 401
- [x] Reward fire-and-forget (#75) — retry logic + logging
- [x] Data retention purge (#76) — daily purge of 30-day-old tip_scores/tip_feedback
- [x] Port mismatch (#77) — fixed in docker-compose + env var config
- [x] UX refinements (#100–102) — "done/snooze/dismiss" feedback only, config page UI, settings gear button

### Phase 3 — Native mobile  *(M3)*
- [ ] iOS app (SwiftUI) with APNs push
- [ ] Android app (Compose) with FCM push
- [ ] `notifier` gains APNs + FCM channels, per-device rate limits
- [ ] Migrate auth from Auth.js to dedicated OIDC provider (trigger from ADR-0004)
- [ ] Consolidate MLflow behind shared OIDC (SSO for all internal services)
- [ ] Decide-and-deliver scheduler: per-user "is this tip worth interrupting now?" threshold

### Phase 4 — MLOps at scale  *(M4)*
- [x] MLflow deployed as external service (`mlops` compose profile); own auth; health check integrated
- [ ] Write first retraining pipeline + first MLflow experiment logging from `ml/serving` + JetStream consumers (#98)
- [ ] Feature-to-prompt pipeline — nightly batch job materializes context for LLM; cuts inline latency (#94)
- [ ] Prompt optimization loop — sim A/B → MLflow experiment → human-approved promotion (#95)
- [ ] LLM fine-tuning — tip reactions as training signal; LoRA on base model; MLflow tracks runs (#96)
- [ ] Embedding-based task clustering — `nomic-embed-text` for dedup + user pattern features (#97)
- [ ] Modular-monolith packaging + import-boundary lint (#47)
- [ ] Consolidate MLflow auth into shared OIDC provider (tracked as M3 issue #85)
- [ ] Shadow → A/B → launch pipeline as first-class in MLflow
- [ ] Online experiments framework: deterministic assignment + bandit policies alongside fixed-split A/B
- [ ] Cross-user collaborative features (opt-in only); cohort slicing; fairness checks
- [ ] Drift monitoring (feature + prediction + reward drift); model cards per LLM version

### Phase 5 — Production hardening  *(M5)*
- [ ] Audit logging, rotation of provider tokens + internal signing keys
- [ ] **k3s** on existing VM, then k8s + HPA once multi-node justified (no cliff)
- [ ] Multi-region failover, Postgres PITR, event-bus mirroring
- [ ] Public integration SDK; sandbox tenancy for third-party connectors
- [ ] Billing + subscription tiers

---

## Contributing

This repo is split into independent modules; most tickets belong to exactly one. Pick an issue, check its milestone (= phase), read the service's `README.md`, ship.

Conventions and per-service guidance live in [`CLAUDE.md`](CLAUDE.md).

## License

All rights reserved — 2026. Contact the owner for licensing inquiries.
(We'll switch to an OSS license for non-sensitive packages once the public SDK lands in Phase 5.)