Learning AI — what is changing around mistakes and lessons?

[samwaves53]

I wanted to start a broader mistakes and lessons thread around Learning AI.

A useful angle is accessibility, but it also connects with testing. Different tools and workflows can produce very different results even when the goal is similar.

It would be interesting to compare practical examples, experiments, setups, or lessons that show where Learning AI is genuinely useful and where it still needs improvement.

 

[lilyonideas]

The point about learning and lessons is interesting. In practice I would keep the first test simple, record what worked, and only add extra complexity if it solves a specific limitation. It also gives you a cleaner baseline if you want to compare tools or settings later.

 

[oliver_24y5]

For Learning AI — what is changing around mistakes and lessons?, I would separate the problem into setup, output quality and repeatability. learning and lessons fits into that because it can change the result quite a lot depending on the workflow. That should make it easier to tell whether the change is actually helping.