- Add proto schemas in packages/shared-types/events/ (oo.events.v1):
envelope.proto, signals.proto, integration.proto
- buf.yaml with STANDARD lint + FILE breaking-change rules
- .gitea/workflows/buf-check.yaml: lint + breaking check on every PR
touching events/ (needs a Gitea Actions runner to execute)
- scripts/buf-check.sh: local equivalent of the CI check
- NormalizedEvent TS envelope gains eventId, schemaVersion, producer
to align with the proto Envelope message
- ml/serving/schemas.py: pydantic models mirroring the v1 proto types
- nats_consumer.py: validate payloads via pydantic instead of raw .get()
A field-rename PR will now fail buf breaking with exit code 100 and
show the offending messages. To make a breaking change: keep the old
field reserved, add the new one, bump schema_version to v2.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- ml/serving/README.md: new — contract, JetStream consumer docs, config,
health story, extraction criteria, state file reference
- ml/README.md: note JetStream consumers in serving/ row
- CLAUDE.md: update active work to reflect #98 shipped, #99 still pending
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Adds a NATS JetStream consumer to ml/serving so the feature pipeline
can react to events without the API triggering every read.
- nats_consumer.py: durable push consumers for signals.> and feedback.>
streams; acks on success, naks for redeliver, up to NATS_MAX_DELIVER
attempts; per-consumer health state (last_msg_ts, processed, errors)
- main.py: FastAPI lifespan wires start/stop; /health exposes nats state
- requirements.txt: adds nats-py>=2.9.0
- Dockerfile.ml: copy all *.py from ml/serving (was missing prompts.py)
Handled subjects:
signals.task.synced → writes per-user sync metadata to STATE_DIR
signals.tip.feedback → logged for observability (reward via HTTP path)
Config: NATS_URL (empty = disabled), NATS_DURABLE_PREFIX, NATS_MAX_DELIVER
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Ship the scaffolding for #99 (phase B.3 of #81):
- ml/serving: add /score/egreedy/v2, /reward/egreedy/v2, /stats/egreedy/v2
endpoints (D=12). New feature dims: completion/dismiss rates, mean dwell
(clipped 10min), preferred-hour alignment (cosine, 1-dim), tip volume (log).
Separate state file per user (_egreedy_v2.json). /reset clears v2 state too.
- ADR-0012: documents D=7→12 dimension change, normalization choices, shadow
rollout protocol, and promotion gate (offline sim win per ADR-0002).
- recommender.ts: register egreedy-v2-shadow in shadow-policy map (disabled by
default). When enabled, calls /score/egreedy/v2 fire-and-forget and publishes
shadow:egreedy-v2-shadow serve signal. No reward to shadow — sim is the gate.
- sim runner/personas: personas carry synthetic profile_features per persona;
_call_score/_call_reward thread profile_features through (None-safe for v1/linucb).
- 18 new Python tests; all 56 Python + 170 TS tests pass.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Centralizes user-level features (completion_rate_30d, dismiss_rate_30d,
mean_dwell_ms_30d, preferred_hour, tip_volume_30d) in a TS registry that
owns both definition and SQL aggregation, since the data lives in the
TS-owned SQLite tables (tip_views/tip_feedback). Lazy TTL refresh keeps
recommend latency bounded; values persist in user_profile_features (KV).
ml/serving accepts profile_features on /score + /generate but does not
yet consume them — extending the bandit feature vector changes D and
resets every user's learned state, so that's a deliberate phase-B step.
Includes ml/features/profile_schema.py as a contract mirror with a sync
test that diffs name sets against registry.ts.
ADR-0011 records the data-locality reasoning (registry in TS, not Python
as the issue originally suggested).
Phase B (deferred): event-driven incremental updates, bandit consumption
with state migration, admin per-user profile page, staleness alerts.
Refs #81.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Replaces the hardcoded "v1" label with a real prompt registry:
ml/serving/prompts.py — keyed by version: v1 (baseline),
v2-mentor (calm/specific persona),
v3-few-shot (v1 persona + curated examples)
ml/serving/main.py — POST /generate accepts optional prompt_version,
422 on unknown, echoes the version actually used
back in the response
services/api/src/config.ts — TIP_PROMPT_VERSION: empty / single / comma-list
(uniform random per request)
services/api/src/routes/recommender.ts
— pickPromptVersion() drives selection; the
response's prompt_version (not a stale TS
constant) is what lands in tip_scores so the
#92 reward-analytics dashboard shows real
per-variant reaction rates
Closes#84.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
ML serving:
- LinUCB contextual bandit (disjoint, d=5 features: hour_sin/cos, is_overdue, task_age, priority)
- /score endpoint replaces stub random; /reward endpoint for online learning
- Per-user model state persisted to disk as JSON (survives restarts)
- venv at ml/serving/.venv; start with pnpm dev from ml/serving
Recommender:
- Todoist fetch now extracts features (is_overdue, task_age_days, priority)
- RemotePolicy calls ml/serving with 3s timeout; falls back to RandomPolicy
- Reward sent to /reward on feedback (done=+1, snooze=0, dismiss=-1)
Web Push:
- VAPID keys in config; push_subscriptions table in DB
- POST/DELETE /api/push/subscribe; GET /api/push/vapid-public-key
- Service worker (public/sw.js): push → showNotification, notificationclick → focus/open
- "notify me" button on tip page; registers SW + subscribes on permission grant
Event bus:
- services/api/src/events/bus.ts: typed EventEmitter wrapper
- Subjects: signals.tip.served, signals.tip.feedback, signals.task.synced
- Same publish/subscribe API NATS JetStream will implement — swap is mechanical
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
