Recent content by runner_liam

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

I think marketing and learning 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.

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