Can anyone share what they're doing with reasoning models? They seem to only make a difference with novel programming problems, like Advent of Code. So this model will help solve slightly harder advent of codes.
By extension it should also be slightly more helpful for research, R&D?
Have been using them for non-interactive coding where latency is not an issue. Specifically, turning a set of many free-text requirements into SQL statements, so that later when an item's data is entered into the system, we can efficiently find which requirements it meets. The reasoning models' output quality is much better than the non-reasoning models like 3.5 Sonnet, it's not a subtle difference.
I found reasoning models are much more faithful at text related tasks too (i.e. 1. translating long key-value pairs (i.e. Localizable.strings), 2. long transcript fixing and verification; 3. look at csv / tabular data and fix) probably due to the reflection mechanism built into these reasoning models. Using prompts such as "check your output to make sure it covers everything in the input" letting the model to double-check its work, avoiding more manual checks on my end.
Seriously? That doesn't require a human?! Are we talking about some kind of "generic" incident? (Type 3: forgot to manually update the xxxx file.) Or what's going on?
By extension it should also be slightly more helpful for research, R&D?