Better weather predictions could improve disaster preparedness and reduce costly forecast errors for agriculture, aviation, and emergency response.
Google DeepMind has introduced WeatherNext 3, an AI weather forecasting model that uses real-time satellite data to provide hourly weather predictions with higher resolution than previous versions. The model addresses a longstanding challenge in weather forecasting: predicting highly local and rapidly changing weather patterns, which has significant impacts on agriculture, supply chains, clean energy production, and national economies. WeatherNext 3 offers improved precipitation forecasting and includes clean energy variables, and is now integrated across Google Search, Gemini, Maps, Google Maps Platform, and Cloud.

AI weather models have spent three years closing the gap with physics-based forecasting, but two problems stayed open: resolution too coarse for local terrain, and initialization tied to numerical weather prediction (NWP) analysis that arrives about six hours late. WeatherNext 3, released by Google DeepMind and Google Research, attacks both. It takes a live global geostationary satellite mosaic as a direct model input, re-initializes every hour, and emits forecasts down to 0.05° (~5 km) while t

Today, OpenAI released GPT-6 Astra. The company calls it its most intelligent and aligned model, and positions it primarily as a computer-use system rather than a chat model. The pitch is that Astra operates software the way a person does, across browsers, spreadsheets, desktop applications and terminals, and finishes multi-step jobs instead of describing how to do them. Is it deployable? Partly, and not on your own hardware. Astra is a closed, hosted model with no released weights, so self
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