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Anthropic Disables Claude Fable 5 and Mythos 5 After US Government Order

Anthropic has disabled its two most capable models for every customer. The shutdown followed a US government export control directive. The order arrived on June 12, 2026. It named Claude Fable 5 and Claude Mythos 5 specifically. Both models had launched only three days earlier, on June 9. The directive cited national security authorities, according to Anthropic. It suspended access by any foreign national, inside or outside the United States. That scope included Anthropic’s own foreign national employees. Anthropic cannot filter foreign nationals from US users in real time. So it shut both models down for everyone to ensure compliance. Access to all other Anthropic models was unaffected. Claude Opus 4.8 and the rest stayed online. So, What Actually Happened Anthropic published a public statement within hours of the order. Commerce Secretary Howard Lutnick sent the letter to CEO Dario Amodei. The letter did not spell out the specific national security concern. ...

A Coding Implementation on Spatial Graph Neural Networks for Urban Function Inference Using city2graph, OSMnx, and PyTorch Geometric

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In this tutorial, we build an end-to-end spatial graph learning pipeline using city2graph . We start by collecting real urban POI data and street network information from OpenStreetMap, with a synthetic fallback to ensure the workflow remains reliable. We then engineer spatial features, construct multiple proximity graph families, and compare how different graph-building strategies represent the same urban environment. After that, we create both heterogeneous and homogeneous graph structures, convert them into PyTorch Geometric format, and train a GraphSAGE model to predict POI categories from spatial structure. Through this process, we integrate geospatial data processing, graph construction, and GNN-based urban function inference into a single practical workflow. Installing city2graph and Importing Geospatial and Graph Learning Libraries Copy Code Copied Use a different Browser !pip -q install "city2graph[cpu]" osmnx contextily scikit-learn 2>/dev/null import w...

Moonshot AI Launches Kimi Work, a Local Desktop Agent Reportedly Running on Kimi K2.6 With a 300-Sub-Agent Agent Swarm

Moonshot AI has introduced Kimi Work, an AI agent that runs on your own desktop. The Beijing-based AI entity announced it this week along with downloads for macOS and Windows. Kimi Work reads local files, drives your real browser, and runs scheduled tasks. It targets knowledge workers whose bottleneck is access to files and live sessions. Most agent tools of the past two years ran in the cloud. You type a goal, a remote server spins up a sandbox, and a hosted browser acts. Kimi Work runs locally instead, reaching files and sessions you already use. What is Kimi Work? Kimi Work is a downloadable application, not a web chat. You give it goals in plain language, and it acts on your machine. Independent community mentions report that it runs on Kimi K2.6, Moonshot’s flagship model. K2.6 is an open-weight Mixture-of-Experts model released on April 20, 2026. It activates about 32 billion parameters per token. It carries a 256K-token context window for long, multi-step ...