Obviously AI blog comment. Transcript follows. This hits different when you're building Al agents that need to handle sensitive data. I've spent months automating workflows where screenshots get processed automatically - debugging sessions, Ul state captures, error logs. The temptation to "just blur the API key" is constant. Your math proof is the perfect reminder that convenience != security. What caught me: the JPEG compression resilience. Most people assume lossy compression destroys the hidden information. Turns out it's shockingly persistent. That's the dangerous part - blur looks secure enough that people trust it, but the math doesn't care about appearances. The practical takeaway for anyone building automation: if you're programmatically redacting screenshots, crop or mask with solid colors. Don't blur. And definitely don't trust "smart blur" plugins that claim to be safer. Also love the pedagogical approach here - showing the 1D case first, then building to 2D + JPEG. Makes the math accessible without dumbing it down. More security writing should do this. Question: have you explored whether modern ML-based "deblurring" models trained on natural images can extract even more information than the algebraic approach? I'd imagine they'd hallucinate plausible details where quantization noise exists, which could be worse than full recovery in some threat models.