It's a black box, but you can run tests to quantify the behaviour and establish, for example, that a certain model is X% more likely to give a certain behaviour.
> Initially I didn’t instruct Claude to use the Socratic method, but that works much better. It’s significantly less “information-dumpy”. When I know a topic well, Claude successfully shortcuts the basics.
> Ignoring requires-python upper bounds. When a package says it requires python<4.0, uv ignores the upper bound and only checks the lower. This reduces resolver backtracking dramatically since upper bounds are almost always wrong. Packages declare python<4.0 because they haven’t tested on Python 4, not because they’ll actually break. The constraint is defensive, not predictive.
My toddler was playing with my Kindle the other day, and he bought a £600 (yes, six hundred) volume of books. I was unable to refund them automatically, and when looking for help I was confronted with a "fuck off" contact page. After finding the option to talk to a human, I was put through within 5 seconds, and the woman had the item refunded in about 1 minute.
I was once working on a project where we had a gRPC server that inserted data into the DB for another service.
This split was probably a mistake, as the interface we exposed resulted in us making twice as many DB calls as we actually needed to.
One of the stored procs needed a magic number as a parameter, which we looked up via another DB query.
One of the other Devs on the team tried to convince me to write a separate gRPC server to run this (trivial) query.
"We're doing microservices, so we need to make everything as small as possible. Looking up this value is a separate responsibility from inserting data."
Luckily our tech lead was sane and agreed with me.