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There are several ways to approach Robot Vision, so I wanted to compare experiences. I am particularly interested in cost control and deployment. I would like to compare approaches that people have actually used rather than just theoretical suggestions. Which approach would you recommend and why?

I think learning and automation is the key part of this topic. A practical approach is to define the outcome you want, test it with a small example, and then check quality, speed and reliability separately. If you share the exact setup, it would be easier to narrow down which part matters most.

I wanted to start a broader ideas worth trying thread around Google Gemini. A useful angle is data quality, but it also connects with collaboration. Different tools and workflows can produce very different results even when the goal is similar. It would be interesting to compare practical...