Interesting ways to use AI Research for creative experiments

I think research and experiments is the key part of this topic. A practical approach is to define the outcome you want, test it with a small example, and then check quality, speed and reliability separately. It also gives you a cleaner baseline if you want to compare tools or settings later.

The point about research and experiments 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 is usually easier to troubleshoot when each change is tested on its own.

The point about research and experiments 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. The original point — "I wanted to start a broader creative experiments thread around AI Research. A useful angle is advanced workflows, but it also connects with customisation. Different tools and work…" — gives a useful starting point for that comparison.