The discovery of an unsecured Chinese policing dashboard shows how authorities track people of interest.

And just like that—surprise!—one AI company bails out another AI company's grift. Google agreeing to rent compute from xAI (cough, "SpaceX") magically makes them eligible for inclusion in the S&P500. Americans, they are looting your life savings, the ones you earned through labour that they are gleefully replacing. Your descendants will never have the chance you had. https://techcrunch.com/2026/06/05/google-will-pay-spacex-920m-per-month-for-compute/

While a few people complain that AI is "stealing Open Source", training on Open Source code and giving nothing in return, I'm currently living the complete opposite: we are in

⚫ Reconstructed tables ⚫ Census ⚫ Online works ⚫ Computing aids ⚫ Pi ⚫ Sustainable digitization ⚫ Nancy The aim of the LOCOMAT project is to make available a number of interesting and/or important historical tables, and to facilitate the study of the original tables by historians of mathematics. An overview of the motivations of the project appears in Denis Roegel, "The LOCOMAT Project: Recomputing Mathematical and Astronomical Tables", IEEE Annals of the History of Computing, April-June 2012, pages 74-79. Slides of an overview of the project can be found here (in French). This page is located at http://locomat.loria.fr/ GDML (Global Digital Mathematics Library)

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On 3 December 1992, in a Vodafone office west of London, a 22-year-old British software engineer named Neil Papworth sat at a desktop computer terminal, typed “Merry Christmas” using full words rather than the now-conventional abbreviations, and pressed send. The message travelled through the Vodafone cellular network and arrived seconds later on a four-and-a-half-pound Orbitel […]

Welcome to Cambridge Core

James "Weston" Higginbotham went missing one week ago while on a family vacation in Japan.

Multimodal Large Language Models (MLLMs) have made substantial progress in egocentric video understanding, but their ability to reason cooperatively from multiple embodied viewpoints remains largely unexplored. We study this problem through multi-robot cooperative dynamic spatial reasoning, where a model must answer spatial, temporal, visibility, and coordination questions by integrating synchronized egocentric videos from a team of moving robots. To support this setting, we introduce CoopSR, the first benchmark for this task, together with EgoTeam, a multi-robot egocentric QA dataset. EgoTeam contains 114,227 QA pairs spanning 19 question types, four difficulty tiers, and three team sizes in Habitat and iGibson, along with a real-world test set of around 2,326 QAs collected using two quadruped robots. We further propose SP-CoR (Spectral and Physics-Informed Cooperative Reasoner), an MLLM framework for fine-grained cooperative spatial reasoning. SP-CoR combines dynamics-aware multi-robot frame sampling, spectral- and physics-guided view fusion, and physics-aligned prompt distillation, enabling the model to benefit from privileged robot-pose supervision during training while requiring only egocentric videos at test time. Across 22 MLLM baselines, SP-CoR consistently improves cooperative reasoning, outperforming the strongest fine-tuned baseline by +3.87% on Habitat and +7.12% on iGibson. It also shows stronger generalization to unseen team sizes and real-world robot tests. Code can be found at https://github.com/KPeng9510/seeing-together.git.

A new series on learning AI, from the ground up

I’ve been experimenting with different approaches to running code in a sandbox for several years now, but my latest attempt feels like it might finally have all of the characteristics …