The story is about vision-language models and how far they’ve come in the last 12 months. Specifically, we'd love to get your thoughts on the following questions: 1) In your opinion, how far have VLMs come in the last 12 months? What has advanced architecturally that has made it possible for VLMs to start entering production workflows? And, are we truly there yet or is there work still to be done making visual understanding reliable enough for production? 2) With so many models now available, how should developers determine which VLM is ideal for their specific use case? Furthermore, given that leaderboards and benchmark wins can often be misleading, what is the best way to evaluate these systems for true accuracy and to distinguish between a model’s failure to "understand" an image versus a failure in the user’s prompt? 3) What are the most common or dangerous "hidden" failure modes you see in current VLMs, and what specific architectural guardrails or failsafes should teams be building into their pipelines to ensure these models don't cause catastrophic mistakes at scale?