Call me princess cruises the
way I ship

PROBLEM

Anyone who talks for a living has hours of voice notes and zero time to reshape them. Off-the-shelf Whisper gives you transcripts. Nobody publishes transcripts.

DECISIONS

• OpenAI Whisper over self-hosted: faster TTM, predictable cost at this volume.
• Vercel Edge for the streaming endpoint — SSE all the way to the browser.
• Prompt-template chain instead of fine-tuning: cheaper to iterate, easier to audit.
• Supabase RLS so user audio is isolated without writing an auth service.

ARCHITECTURE

browser
  │ upload .m4a (chunked, resumable)
  ▼
Vercel Edge fn ── signed URL ──> S3
  │
  ▼
Whisper API ── transcript ──> prompt chain
                              │
                  ┌───────────┼───────────┐
                  ▼           ▼           ▼
              X thread    LinkedIn    YouTube
              (SSE)       (SSE)       script
                  ▼
              Supabase  ─ RLS per user ─ history

OUTCOME

• Public product live with auth, pricing, and script generation flows.
• Typical 5-minute memo to multi-format drafts: under ~30 seconds in normal runs.
• Generation cost kept to low cents per script at current usage patterns.

PROBLEM

Consumer finance apps tell you what moved. They rarely tell you why, and the explanations they do give are either too shallow or paywalled behind a Bloomberg terminal.

DECISIONS

• FastAPI for the ingest tier — async websockets, typed contracts, fast iteration.
• TF-Serving for inference: models are pre-trained, inference-only; no PyTorch needed.
• Supabase for auth + a Postgres feature store; saved me a quarter of plumbing.
• Server-side rate-limiting on the LLM explainer — cost is the real adversary.

ARCHITECTURE

Polygon WS  ─┐
                │
Tiingo REST ─┼─> FastAPI ingest ─> Postgres
                │                          (feature store, RLS)
                ▼                                │
        TF-Serving ─<─────────────────────┘
                │
                ▼
        LLM explainer ─ retrieves features ─> "why it moved"
                │
                ▼
        React ── WS push ── live tickers + cards

OUTCOME

• 1k+ liquid US tickers supported in the research/watchlist universe during testing.
• Sub-second model inference for cached feature lookups; LLM explanations return in seconds.
• Dev/demo infra kept under ~$100/month before paid market-data costs.

PROBLEM

Brilliant AI researchers built a cool model. It needed tens of millions of pieces of data to train on.

ROLE

• Built end-to-end ETL pipelines to tactically acquire public data, generate synthetic data, and transform real and synthetic data for model use.
• Used Modal workers and S3 handoffs so acquisition jobs could scale without becoming one giant long-running service.
• Wired CloudWatch visibility around runs because silent data failures are how models get haunted.

ARCHITECTURE

              Scheduler
                  │
                  ▼
        Modal worker fleet ── rotating proxies ──> public sources
                  │
   synthetic gen ─┤
                  ▼
        Raw S3 partitions ── normalize + dedupe ──┐
                                                  │
                                                  ▼
                                       Training-ready S3
                                                  │
                                                  ▼
                                       CloudWatch ── logs + alerts

OUTCOME

• 1M+ rows/day acquired and transformed.
• Checkpointed workers made failed runs safe to resume.
• Raw and normalized datasets were separated for auditability.