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I wanted to start a broader productivity ideas thread around Kotlin. A useful angle is automation, but it also connects with best workflow. Different tools and workflows can produce very different results even when the goal is similar. It would be interesting to compare practical examples...

A useful way to look at What is the best way to approach future improvements for Deep Learning? is to focus first on best and future. That tends to make the trade-offs easier to compare. It is usually easier to troubleshoot when each change is tested on its own.

For AI Avatars, 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...