Data Science Weekly - Issue 664

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Data Science Weekly - Issue 664<br>Curated news, articles and jobs related to Data Science, AI, & Machine Learning<br>Data Science Weekly<br>Aug 13, 2026

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Issue #664<br>Aug 13, 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

Building Bibliographic Superwork Clusters for Discovery with Local LLMs<br>Blog post about building bibliographic superwork clusters to help in catalog discovery. An experiment in using a local LLM to help label how books relate to each other…

A calculator, compiled into a transformer<br>I turned off reasoning and gave six frontier models the same 500 random five-digit multiplication problems. Each model had to answer directly, with no scratchpad work along the way. Most struggled: five of the six scored below 30%, and none reached 90% accuracy…Most research on this gap has approached it as a problem of learning. I wondered about a different route: could I build a transformer directly from the same basic algorithms taught in grade school?…

Making 768 servers look like 1<br>The most difficult infrastructure component to scale is almost always the database. A single database server cannot handle such demand, so we must spread the queries and data out across many servers with database sharding...Database sharding is the best way to scale a Postgres or MySQL database for anything beyond a few terabytes of data. Let’s look at how we go from a small single-node database, to one with a few terabytes spread across four shards, all the way up to one that is sharded across 768 servers and storing a petabyte of data…

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Data Science Articles & Videos

Airbnb’s Eval-driven development: Lessons from evaluating GenAI at scale<br>At Airbnb, we build LLM-powered features across our product, with recent launches including review highlights, AI customer support, smart communication features for guests and hosts, and more. Behind the scenes, we also use AI to help us spot trends and understand what’s working, guiding where we improve the product next. Each product team may have its own evaluation criteria, process, workflows, etc. However, these are built on top of some common foundations and principles. An infrastructure team provides tooling and best practices, incorporating learnings across domains so that they are shared with everyone building products at Airbnb. In this article, we wanted to share some of these best practices and learnings with the broader engineering community…

Compression is prediction<br>I was reading about compression recently when I stumbled upon something crazy: that compressors and LLMs are, at their core, trying to solve the exact same problem. In this post, I’m going to walk us through the basics of compression to understand its deep relationship with language modeling…

Why Fair Coins Tend to Land on the Side They Started — A Wobbly Coin Flip Simulator<br>A few years ago, my colleagues and I decided to test the Diaconis-Holmes-Montgomery (DHM) hypothesis that a fair coin, when flipped in the air and caught in the hand, tends to land on its starting side slightly more often than 50% (Diaconis, Holmes, & Montgomery, 2007). In fact, DHM suggested that the effect would be about 1%, and they indicated that in order to detect such a small effect, a diagnostic empirical test would have to feature about 250,000 tosses. As a group, we ended up flipping a series of coins for a total of 350,757 times. In the end, the data strongly supported the DHM hypothesis, albeit with two nuances: the size of the effect depends on the person flipping the coin, and the effect appears to wane with practice. A two-page summary is here, and the full paper is here…

City2Graph: Geospatial Graphs for Network Analysis and GNNs<br>City2Graph is a Python library that turns buildings, streets, public transport feeds, origin–destination matrices, zones, and points of interest into spatial and heterogeneous graphs. It bridges GeoPandas, NetworkX, and PyTorch Geometric so the same geospatial data can support network analysis, urban research, and Graph Neural Networks (GNNs)…

Why AIs (Might) Obey the Law<br>A growing body of work empirically tests whether AI models when acting as agents comply with or violate applicable law, including corporate law, tort law, labor law, property law, and contracts. In this paper, we explore a related question: examining why AI models might obey the law. To this end, we draw on and extend the methods for measuring legal compliance pioneered in Tom Tyler’s seminal work, Why People Obey the Law (1990, 2006). Across three studies, we adapt Tyler’s survey methodology—which was originally devised to study the factors explaining human subjects’...