Recent content by jamesbra

For Mistral Models, it helps to think in terms of inputs, constraints, verification, and iteration. One important area is useful tools. 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...

One thing worth trying with llama.cpp: future possibilities ideas and experiences is a controlled comparison around llama.cpp and future. Use the same input for each test and note what changes in the output rather than judging from a single run. That should make it easier to tell whether the...

A practical understanding of AI Podcasting starts with knowing what the system does well, where it is unreliable, and how to validate the result. 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 so...