feat(infra,database): DO deploy + memory schema

infra: Dockerfile (backend from monorepo root) + DigitalOcean App Platform
spec with health check and secret env vars. database: MongoDB Atlas + Voyage
vector-memory schema and the continual-learning loop notes.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

database/README.md

@@ -0,0 +1,32 @@
+# database
+
+Continual-learning memory for PodMan: **MongoDB Atlas** for the team model and
+outcomes, **Voyage** embeddings for vector recall. This is what makes PodMan
+"more useful the more you use it" (the track requirement).
+
+## Collections
+
+| Collection       | Holds                                          | Notes                         |
+| ---------------- | ---------------------------------------------- | ----------------------------- |
+| `pods`           | `Pod` docs (members, repo)                     | one per pod                   |
+| `observations`   | `EngineerContext` snapshots over time          | sampled, append-only          |
+| `collisions`     | detected `Collision`s                          | for replay + precision tuning |
+| `interventions`  | `Intervention`s + outcome (accepted/dismissed) | drives the self-tuning policy |
+| `memory_vectors` | Voyage embeddings of file/feature notes        | Atlas Vector Search index     |
+
+Types live in [`shared/`](../shared/src). Each engineer/file/feature note is
+embedded with Voyage and stored alongside its source doc for retrieval by the
+PodMan brain.
+
+## The continual-learning loop
+
+1. **Observe** → write `observations`.
+2. **Store** → embed notes into `memory_vectors`.
+3. **Predict** → collision detector + brain decide whether to intervene.
+4. **Outcome** → update the `interventions` doc with accepted/dismissed.
+5. **Adapt** → tune thresholds + ownership attribution from outcomes.
+
+## Setup
+
+Create an Atlas cluster + a Vector Search index on `memory_vectors.embedding`,
+then set `MONGODB_URI` and `VOYAGE_API_KEY` in `.env`.