Kartikeya
da5b2622b2
Promote all 12 staged canonical files from docs/generated/files/ to their
live paths, creating the full PodMan architecture:
Backend:
- server.ts: HTTP service (token mint, sync-PR, outcome recording, /health, WS relay)
- agent.ts: worker joining LiveKit room, grabbing screenshare frames at ~1fps
- agent/podman.ts: orchestrator loop (vision -> collision detection -> intervention)
- env.ts: flat env var accessors replacing nested stub
- vision/gemini.ts: JPEG -> Gemini vision -> EngineerContext (real implementation)
- collision/detector.ts: fused vision+GitHub collision detection (the moat)
- github/client.ts: Octokit wrapper with caching + sync PR creation
- memory/store.ts: extended with recordObservation/recordCollision/recordIntervention/recordOutcome helpers
- memory/vectors.ts: stub for Voyage+Atlas vector recall (Loop A)
- memory/policy.ts: stub for intervention policy gate (Loop B)
- voice/live.ts: stub for Gemini Live TTS voice output
Shared:
- messages.ts: LiveKit data-channel wire protocol (DataMessage, InterventionOutcome, TeamModel, LocalGitReport)
- index.ts: re-exports messages module
Frontend:
- livekit/useScreenPublish.ts: hook for joining pod and publishing screenshare
- livekit/useInterventions.ts: hook for receiving collision cards and responding
- lib/api.ts: fetchToken + postOutcome HTTP helpers
Database:
- database/init.ts: MongoDB Atlas collections + indexes + vector search index
Infra:
- infra/.do/app.yaml: DO App Platform spec (static_site + service + worker)
Retire stubs superseded by canonical decomposition:
- backend/src/index.ts (replaced by server.ts)
- backend/src/intervention/engine.ts (logic now in agent/podman.ts)
- backend/src/livekit/token.ts (token minting now in server.ts)
Install missing dependencies: @livekit/rtc-node, sharp, mongodb, ws, @types/ws
Type error fixes:
- vision/gemini.ts: use MediaResolution.MEDIA_RESOLUTION_LOW enum value (not string literal)
- agent/podman.ts: wrap SuggestedActionKind into { kind: action } SuggestedAction object
All packages pass pnpm -r typecheck and pnpm -r build.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
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 Collisions |
for replay + precision tuning |
interventions |
Interventions + outcome (accepted/dismissed) |
drives the self-tuning policy |
memory_vectors |
Voyage embeddings of file/feature notes | Atlas Vector Search index |
Types live in shared/. 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
- Observe → write
observations. - Store → embed notes into
memory_vectors. - Predict → collision detector + brain decide whether to intervene.
- Outcome → update the
interventionsdoc with accepted/dismissed. - 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.