Recent content by oliviaonpixels

For Research Agents, it helps to think in terms of inputs, constraints, verification, and iteration. One important area is limitations. 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...

The point about students and troubleshooting 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. That way you can improve the workflow without losing track of what caused the improvement.

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