Recent content by liamonfocus

I wanted to start a broader practical use cases thread around AI Troubleshooting. A useful angle is advanced workflows, but it also connects with data quality. Different tools and workflows can produce very different results even when the goal is similar. It would be interesting to compare...

I think startups and way 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.

A useful way to understand Make is to separate the goal, the input, the model or tool, and the way the output is evaluated. One important area is customisation. Define what a good result looks like before changing tools or settings, then test one variable at a time so you can tell what actually...