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From hallucinations to hardware: Lessons from a real-world computer vision project gone sideways
What we tried, what didn't work and how a combination of approaches eventually helped us build a reliable computer vision model.
The idea was simple: Build a model that could look at a photo of a laptop and identify any physical damage — things like cracked screens, missing keys or broken hinges. Blending different approaches beats relying on just one: The combination of precise, agent-based detection alongside the broad coverage of LLMs, plus a bit of fine-tuning where it mattered most, gave us far more reliable outcomes than any single method on its own. What started as a simple idea, using an LLM prompt to detect physical damage in laptop images, quickly turned into a much deeper experiment in combining different AI techniques to tackle unpredictable, real-world problems.
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