Data Science Weekly - Issue 662

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Data Science Weekly - Issue 662<br>Curated news, articles and jobs related to Data Science, AI, & Machine Learning<br>Data Science Weekly<br>Jul 30, 2026

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Issue #662<br>July 23, 2026

Hello!<br>Once a week, we write this email to share the links we thought were worth sharing in the Data Science, ML, AI, Data Visualization, and ML/Data Engineering worlds.

And now…let’s dive into some interesting links from this week.

Editor's Picks

How to make a mesh dance in real time to music<br>Audio transforms into a frequency space, where filtering becomes easy. The nice surprise is that meshes have a frequency space too…Take a drum head. Hit it and it doesn’t wobble arbitrarily; it rings in a discrete set of standing waves whose shapes are determined entirely by the drum’s geometry. Mark Kac asked in 1966 whether you could run that backwards: can one hear the shape of a drum?…On a triangle mesh those standing waves are the eigenvectors of the discrete Laplacian, its manifold harmonics. Low eigenvalues are slow global wobbles, high eigenvalues fine ripples: the same low-to-high ordering an FFT gives you, over a surface instead of over time…With this you can let the song drive the shape and get Kac’s question the other way. instead of hearing a shape, you see a sound…

Random Selection and Democracy in Athens and Asimov<br>Let’s look at some aspects of random selection as it relates to democracy through these examples. This post contains discussion questions and activities that could be used for a high school or undergraduate class discussion about the “fairness” of simple random, stratified, and systematic sampling — and of nonrandom selection methods. Learning objectives include deepening understanding of these sampling methods, distinguishing between proportional and disproportional allocation, calculating sampling weights for a stratified sample, and relating advantages and disadvantages of methods for political candidate selection to statistical principles of randomization. The footnotes contain some questions for an advanced undergraduate or graduate-level sampling class…

London’s most equidistant pub<br>London’s most equidistant pub is The Greene Man at 383 Euston Road, on the corner of Great Portland Street. Starting from where each of the 33 boroughs’ residents actually live, and arriving by 7pm on an ordinary September Thursday by public transport, journeys to it span 47 minutes between the nearest borough and the furthest, and no borough needs more than 70. No pub in London does better on both measures….The bigger finding is what nobody manages: not one of London’s 3,170 pubs is within an hour of all 33 boroughs….

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Data Science Articles & Videos

You Could Have Come Up With Kimi Delta Attention<br>In this post we are going to walk through the DeltaNet family of linear attention variants, two of which are used by the latest Qwen and Kimi model families, and show how you might have arrived at the same equations by asserting simple things about your hidden state. That is the route we will take: softmax attention  →  linear attention  →  DeltaNet  →  Gated DeltaNet  →  KDA Only after deriving KDA will we turn to the recurrent and chunkwise Triton programs that execute it…

Anyone else measure team health by how long it takes a non-technical person to answer a basic data question? [Reddit]<br>had a client once where the ops manager could just... answer things. revenue by region, churn this month, whatever. no ticket, no slack message to the data team, just opened metabase and pulled it herself i’ve been thinking about that a lot lately because most places i go into are nothing like that. analyst gets pinged for stuff that really shouldn’t need an analyst…

Binary vector embeddings are so cool<br>Vector embeddings by themselves are pretty neat. Binary quantized vector embeddings are extra impressive. In short, they can retain 95+% retrieval accuracy with 32x compression and ~25x retrieval speedup. Let’s get into how this works and why it’s so crazy…

Some useful equations for biological processes<br>Because mathematical modeling plays such a central role in biology and many other scientific disciplines, biologists need to be familiar with the most important mathematical functions. More importantly, they need to be able to “read” these functions and use their parameters to describe, interpret, and quantify biological processes. With this aim in mind, I have compiled a collection of the mathematical functions most commonly encountered in biology, explaining the meaning of their parameters, with particular emphasis on their biological interpretation rather than their mathematical properties…

A collection of self-starters for nonlinear regression in R<br>Usually, the first step in every nonlinear regression analysis is to select the function that best describes the phenomenon under study. The...