I wanted to start a broader creative experiments thread around AI APIs.
A useful angle is data quality, but it also connects with real-world use. 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 APIs is genuinely useful and where it still needs improvement.
A useful angle is data quality, but it also connects with real-world use. 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 APIs is genuinely useful and where it still needs improvement.