Apple-Silicon

In the last two posts (about perceptual hashes in the context of Chat Control and about their adversarial collisions), we explored how 40 lines of Python can solve a problem that the industry often deploys at a global scale. Perceptual hashes are the canonical example of a cheap deterministic layer: no models, no GPUs, no heavyweight dependencies. Hashes handle duplicate and similarity detection. But in a real-world image processing pipeline, there’s work that classical algorithms can’t do: detecting bounding boxes of arbitrary watermarks, filling gaps with inpainting that respects visual context, classifying whether a face is of an adult, or extracting semantic attributes. This work requires large neural networks. ...

Google published TurboQuant on March 24th. Within 48 hours the paper had 575 points on Hacker News, Micron’s stock dropped $900 million, and TechCrunch compared it to Pied Piper’s algorithm from Silicon Valley. One month later, the hype fog has cleared enough to answer the only questions that matter: Does it work? Can I use it today? And the one nobody wants to ask: Is it actually new? What TurboQuant promises (30-second recap) If you already read my previous article on the math, skip this section. ...