I wanted to start a broader mistakes and lessons thread around Full-Stack AI Development.
A useful angle is evaluation, but it also connects with limitations. 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.
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A useful angle is evaluation, but it also connects with limitations. 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.
Photo by on
