🪟 Windows 11 introduces always-on AI agent with access to personal folders
This marks another step in Microsoft’s deep integration of AI into its OS core. While it promises smoother automation, it also raises legitimate questions about user data access, privacy boundaries, and how autonomous these background agents should be.
Microsoft is testing a new Windows 11 feature called ‘Agent Workspace’—an AI-native layer that lets intelligent agents run persistently in the background with access to user directories like Desktop, Documents, and Pictures. Each operates in an isolated Windows session with limited privileges, but Microsoft warns of potential privacy, security, and performance implications. The feature is currently available for Windows Insiders in the Dev and Beta Channels.
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🌦️ WeatherNext 2 — DeepMind’s next-gen AI weather model
WeatherNext 2 showcases how generative AI is reshaping scientific forecasting, pointing to a future where machine learning replaces classical physics models for faster, data-centric climate prediction.
Google DeepMind has announced WeatherNext 2, its most advanced AI-driven weather forecasting model to date. Using a Functional Generative Network (FGN), it generates hundreds of probabilistic weather scenarios in under a minute, introducing controlled randomness to improve realism. The model surpasses previous systems in predicting temperature, wind, and humidity up to 15 days ahead and demonstrates how generative AI can outperform traditional physics-based meteorological methods in both speed and scale.
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🕸️ Exploring the Epstein emails through an interactive graph
An impressive blend of AI, data visualization, and investigative journalism — a great example of using public data and LLMs to promote transparency and fact-based analysis.
The Epstein Document Explorer is an open-source visualization platform that maps connections between individuals, locations, and events extracted from the Epstein email dataset. Powered by large language models, it builds an interactive graph with filters and timelines that allow users to explore relationships and verify source documents in context.
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🗺️ An official atlas of North Korea
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🤖 The productivity impact of AI coding agents
This research brings data-driven insight to the debate over AI-assisted coding — suggesting such tools enhance the capabilities of seasoned engineers rather than replace human expertise.
A University of Chicago study by Suproteem Sarkar examined how Cursor’s AI coding agent affects developer productivity across thousands of teams. The research showed a 39% increase in merged pull requests after the agent became the default tool. Senior developers tended to use the agent’s suggestions more strategically, while bug rates and code sizes remained stable — implying the boost came from workflow efficiency, not code inflation or quality loss.
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