Recent content by visionryan

When working with AI Troubleshooting, the biggest gains usually come from treating it as a repeatable workflow rather than a one-off prompt or experiment. One important area is creative uses. Define what a good result looks like before changing tools or settings, then test one variable at a...

I think local and llms 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. That way you can improve the workflow without losing track of what caused the improvement.

For n8n, it helps to think in terms of inputs, constraints, verification, and iteration. One important area is advanced workflows. 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...