With Pixeltable, your schema, computed columns, vector indexes, and HTTP routes live in a single declarative app.py — no separate vector DB, no for row in ...: model loops, and no imperative glue:

import pixeltable as pxt
import pixeltable.functions as pxtf
from pixeltable.functions.openai import embeddings
from pixeltable.serving import FastAPIRouter

TableModel = pxt.model_base()


@pxt.udf
def excerpt(text: str, n: int = 120) -> str:
    return text if len(text) <= n else f'{text[:n]}...'


class Docs(TableModel, name='docs'):
    id = pxt.Column(value=pxtf.uuid.uuid7(), primary_key=True)
    title: pxt.String
    body: pxt.String
    image: pxt.Image | None
    summary = excerpt(body)

    __indexes__ = [
        pxt.EmbeddingIndex(column=body, string_embed=embeddings.using(model='text-embedding-3-small'))
    ]


api = FastAPIRouter(name='ingest')
api.add_insert_route(Docs, path='/docs', inputs=[Docs.title, Docs.body, Docs.image], outputs=[Docs.id, Docs.summary])

Run locally or deploy the exact same app.py to Pixeltable Cloud:

# Local
pip install 'pixeltable[serve]'
pxt init
pxt schema update app.py my_app
pxt service update app.py my_app -f

# Pixeltable Cloud
pxt login
pxt db update pxt://org:db -f
pxt schema update app.py pxt://org:db -f
pxt service update app.py pxt://org:db -f

Whether designing multimodal infrastructure or reviewing pull requests with challenge-the-design and marcel-review, I optimize for a few non-negotiable invariants:

1. State the problem and a measurable falsifier first. Separate true requirements from inherited assumptions before writing code.
2. Delete unnecessary work before adding abstractions. Run a behavior-preserving deletion pass across the whole ensemble (callers, state, and tests) before introducing new layers.
3. Every abstraction must earn its keep. A class or function should represent a crisp contract that can be stated in one sentence—and its docstring should state only what the signature does not.
4. Declarative state over imperative glue. If progress and lineage live in rows and computed columns rather than ephemeral in-memory loops, retries, caching, and observability become properties of the table engine.

• Pixeltable — Co-Founder & CEO · Building open-source multimodal AI data infrastructure and Pixeltable Cloud (pxt://).
• Noteable (acquired by Confluent) — Co-Founder · Built the collaborative data notebook platform bridging Python, SQL, and interactive analytics, and pioneered early ChatGPT Code Interpreter / Notebook plugin workflows.
• Amazon & AWS — Decision Scientist & Product/Data Leader · Applied econometrics, optimization, and large-scale cloud data systems across AWS and Amazon consumer/infrastructure teams.
• Siemens & Structural Engineering — Started my career in structural engineering and industrial systems, where load paths, safety margins, and failure modes are calculated before anything is built.