[valentinconan]

I haven't yet tried to solve truly complex algorithmic problems.

Generally speaking, if the problem is common, the model has likely already been trained to solve it.

If it's truly complex and/or specific to my needs, I can try using a reasoning model to think through a solution before moving on to implementation.

I use the agent to conduct research, find resources to understand the complexity, best practices, feedback, etc., and to write a Markdown analysis file on the topic.

Then I can use this file as a basis to precisely define what I want to do and brainstorm with the agent in thinking mode. The more the task is described and defined, the more accurate the result will be.