Recent content by arthurbyte68

I wanted to start a broader mistakes and lessons thread around DeepSeek Models. A useful angle is cost control, but it also connects with future improvements. Different tools and workflows can produce very different results even when the goal is similar. It would be interesting to compare...

I think general and game 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 is usually easier to troubleshoot when each change is tested on its own.

For AI Music, it helps to think in terms of inputs, constraints, verification, and iteration. One important area is data quality. 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 improved the outcome. Another...