I wanted to start a broader mistakes and lessons thread around Full-Stack AI Development.
A useful angle is useful tools, but it also connects with quality versus speed. Different tools and workflows can produce very different results even when the goal is similar.
It would be interesting to compare practical examples, experiments, setups, or lessons that show where Full-Stack AI Development is genuinely useful and where it still needs improvement.
A useful angle is useful tools, but it also connects with quality versus speed. Different tools and workflows can produce very different results even when the goal is similar.
It would be interesting to compare practical examples, experiments, setups, or lessons that show where Full-Stack AI Development is genuinely useful and where it still needs improvement.