Recent content by dylan_logic

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

The point about news 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 also gives you a cleaner baseline if you want to compare tools or settings later.

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