I wanted to start a broader mistakes and lessons thread around Open Source AI.
A useful angle is quality versus speed, but it also connects with deployment. 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 Open Source AI is genuinely useful and where it still needs improvement.
A useful angle is quality versus speed, but it also connects with deployment. 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 Open Source AI is genuinely useful and where it still needs improvement.