I love seeing these type of use cases for AI. Imagine getting an extra day to prepare before a major cyclone hits. Google DeepMind's WeatherNext model is delivering more accurate cyclone forecasts while being open sourced for researchers and forecasters to build on. • More than 24 hours of added forecast lead time • Three-day predictions matching older two-day forecasts • Track, intensity, and wind structure in one model • 1,000 possible storm scenarios per cyclone • 15-day forecasts in under one minute on a TPU • Training across nearly 5,000 historical storms • Open-source weights and code for wider experimentation • Potential uses in disaster response, renewable energy, and infrastructure planning What would you guys build if you could connect this kind of forecasting AI to automated alerts, logistics, or local response workflows? Read the full article here: https://deepmind.google/blog/weathernext-ai-model-achieves-breakthrough-in-forecasting-cyclones

Opened Claude today... let's see what Opus 5 is all about! https://www.anthropic.com/news/claude-opus-5

Don't let AI-written code go to team review until you can pass a 5-question quiz on what it does, no cheating. It's a speed regulator, catches gaps in understanding you'd otherwise miss, and keeps you moving at the speed of comprehension instead of just the speed of output. I just thought this was an interesting take. Watch video here: https://www.tiktok.com/t/ZTAFDYHpH/

The open source AI world just got a new heavyweight and it's a big one. Moonshot AI just dropped Kimi K3, a 2.8 trillion parameter model they're calling the world's first open "3T class" model, and it's punching well above its weight class. - First open model to hit 2.8T parameters, setting a new bar for open model size - Built on two new architecture pieces (Kimi Delta Attention and Attention Residuals) that change how info flows through the model - Uses a sparse Mixture of Experts setup, only activating 16 out of 896 experts per token instead of the whole model - Native vision built in, plus a 1 million token context window - Claims a 2.5x jump in scaling efficiency over their last model, Kimi K2 - Still trails the top closed models (Claude Fable 5 and GPT 5.6 Sol) on raw performance, but the gap is shrinking - In one demo, it designed a working computer chip in a single 48 hour autonomous run with no human help - In another, it finished astrophysics research in about 2 hours that would normally take a human researcher 1 to 2 weeks - API pricing starts at $0.30 per million tokens for cached input, noticeably cheap for something this size - Full model weights are set to release by July 27 Open models are starting to close the gap on the closed frontier labs! Link: https://www.kimi.com/blog/kimi-k3

This one's a bit late. But in case you don't know, Sonnet 5 is the new default in Claude. It's a very capable model 👍