Speakers

If you use modern FastAPI, you’ve probably written Annotated[...] because the documentation told you to. It works, but what is actually happening inside those brackets?

Before Annotated, FastAPI and Pydantic had a slightly awkward habit: they lied to the type checker. It was a useful lie, but a lie nonetheless.

We’ll see how that trick worked, what changed with Annotated, and how FastAPI and Pydantic turn metadata into dependency injection, validation, and reusable definitions. Along the way, we’ll follow metadata from a type annotation to runtime behavior and see when the same pattern is worth using in your own library APIs.

For most of my career, my job was writing code.

Today that’s a small part of what I do: AI can take care of the implementation. My job now is to formulate the problem for the AI to solve—choose the metrics to optimize and the constraints to respect—then make it fast and easy for the AI to iterate.

This sometimes feels like herding the model: cutting off all the bad routes, so its randomness can only push in the direction of the improvement I want.

In this talk, I’ll review three real-world projects through this lens and propose practical ways to set up feedback for the model.

This is not a software-factory pitch. I still write code, and my review is often the most important constraint, but I don’t think it makes sense to pretend that me writing and reading all the code is the best way to build software anymore.

Luckily, in a world where AIs write the code, the human who formulates the problem is more valuable than the human who wrote the code was a year ago.

Ben will cover the different methodologies used to deliver data at the Space Telescope Science Institute, including streaming database responses, S3 redirects, asynchronous Celery workers, and NGINX.