I am surprised Gemini 3.1 Pro is so high up there. I have never managed to make it work reliably so maybe there's some metric not being covered here.

For example what happens if I use a different async backend like Tokio?

So this article seems surprising since it emphasizes more the non prompt optimization aspects. If that was the selling point I would rather use something like Pydantic AI when I already use Pydantic for so much of the rest.

Big fan of FastAPI but I think SQLModel leads to the wrong mental model that somehow db model and api schema are the same.

Therefore I insist on using SQLAlchemy for db models and pydantic for api schemas as a mental boundary.

I think we are going through the same cycle of http leading to https, the rise of oauth and oidc. Its just way faster now.

Now it feels like it was just lack of competition. Maybe now other countries will start producing lines and trains capable of 400 Kph and hopefully its not a China only thing going forward.

Now its mostly about models getting better.

All said and done, that its even possible is remarkable. Maybe these all go into training the next Opus or Sonnet and we start getting models that can create efficient compilers from scratch. That would be something!

It's an endless stream of basic tool/library questions. Put me off reddit quite a bit.

But switching over to using coding agents we never did the same. Feels like building an eval set will be an important part of what engg orgs do going forward.

I got tired of setting individual allow lists for each CLI, hopefully now I can run them all in Yolo mode while fence does the centralized sandboxing.

But the actual experience with developing on VSCode with Dev Containers is not great. It's laggy and slow.