🎨 Why CIELAB Doesn’t Improve the Median Cut Algorithm
An insightful technical exploration connecting computer graphics with perceptual modeling — a must-read for those optimizing visual algorithms or compression methods.
This article explores why converting images to the CIELAB color space does not enhance the median cut algorithm for color quantization. Through experiments comparing CIELAB, sRGB, and Oklab, the author finds that median cut performs best when the lightness component is weighted more heavily than chromatic channels. The study concludes that perceptual color spaces are better suited for color mapping tasks rather than for the median cut clustering process itself.
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🤖 The AI Collapse Pre-Mortem
A sharp, historically informed reflection that grounds AI optimism in realism — a balanced take for anyone navigating the industry’s next cycle.
This essay considers the possibility of an imminent AI market downturn. It argues that while today’s large language models may be overhyped and commercially unsustainable, their technological core remains groundbreaking. The piece distinguishes hype from substance, noting that real-world AI applications—such as speech recognition, translation, and medical analysis—already achieve or exceed human performance. Even if the market bubble bursts, AI’s underlying innovation will continue advancing.
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📚 Public Montessori Programs Boost Learning at Lower Cost
A compelling, data-driven case for scaling Montessori in public education — combining rigorous evidence with fiscal sustainability.
A large-scale randomized study across 24 public schools found that Montessori preschool students significantly outperformed peers in literacy, memory, and executive function by kindergarten’s end. Despite stronger outcomes, Montessori programs operated at around $13,000 less per child, offering substantial cost efficiency. The results suggest Montessori education yields lasting academic and social advantages, particularly for disadvantaged children.
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