Recent content by driversarah

For DeepSeek, it helps to think in terms of inputs, constraints, verification, and iteration. One important area is privacy considerations. 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...

For Understanding Research Agents: learning curve and productivity, I would separate the problem into setup, output quality and repeatability. research and agents fits into that because it can change the result quite a lot depending on the workflow. It also gives you a cleaner baseline if you...

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