Recent content by luke15

For Swift, it helps to think in terms of inputs, constraints, verification, and iteration. One important area is real-world use. 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. Another...

One thing worth trying with A practical overview of Learning AI for data quality is a controlled comparison around learning and data. Use the same input for each test and note what changes in the output rather than judging from a single run. If you share the exact setup, it would be easier to...

I wanted to start a broader setup showcase thread around Fine-Tuning. A useful angle is privacy considerations, but it also connects with getting started. Different tools and workflows can produce very different results even when the goal is similar. It would be interesting to compare...