I wanted to start a broader mistakes and lessons thread around AI Projects.
A useful angle is troubleshooting, but it also connects with common mistakes. 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 AI Projects is genuinely useful and where it still needs improvement.
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A useful angle is troubleshooting, but it also connects with common mistakes. 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 AI Projects is genuinely useful and where it still needs improvement.
Photo by on
