Recent content by ivy_south

For Mistral AI, it helps to think in terms of inputs, constraints, verification, and iteration. One important area is performance. 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 Computer-Use Agents — what is changing around productivity ideas? is a controlled comparison around computer-use and agents. Use the same input for each test and note what changes in the output rather than judging from a single run. It is usually easier to...

A practical understanding of Learning AI starts with knowing what the system does well, where it is unreliable, and how to validate the result. One important area is common mistakes. Define what a good result looks like before changing tools or settings, then test one variable at a time so you...