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Our WeatherNext 2 AI model from @GoogleDeepMind and @GoogleResearch can predict tropical cyclones with an extra day of lead time. Now, we’re open sourcing the model. Published in @Nature, Google researchers demonstrate how it delivers roughly a decade's worth of weather forecasting progress in a single jump. Here's how it works:

DeepSeek-V4-Flash-0731 debuts with an ECI of 153, comparable to GLM 5.2 and roughly midway between Opus 4.5 and Opus 4.6. It's the second strongest open-weights model available today, behind only Kimi K3.

‼️ BREAKING: An active npm supply chain attack has compromised at least 868 packages carrying over 2 billion monthly installs with a credential-stealing worm. Shai-Hulud is back. It started with the compromise of the GitHub account of the maintainer behind keyv, a library with roughly 127 million weekly npm downloads. A preinstall hook fires on npm install and drops a stealer that sweeps npm, GitHub, AWS, Kubernetes and Vault secrets, and then spreads to more maintainers.

An internal version of our next major model produced 10 new results on long-standing open problems in mathematics and theoretical computer science, using roughly $2,000 worth of tokens at GPT-5.6 Sol API rates.

Today, I want to share something that happened to me on Kraken. I’m completely new to crypto, and this was my first time using Kraken. All I wanted to do was convert USDC into USD. Unfortunately, because USDT and USD differ by only one letter, I accidentally selected USDT instead of USD. I didn’t realize my mistake until the transaction had already been completed. In less than a minute, a single misclick resulted in a loss of approximately 810 USDT—more than $800. To be honest, I was shocked and devastated. Like many newcomers, I assumed that all major stablecoins were worth roughly the same, so converting between them would involve little or almost no loss. I never imagined that such a simple mistake could instantly cost me more than 1% of my funds. I immediately contacted Kraken Support, hoping they would understand that this was an honest human error and review my case, or at least consider making a one-time exception. What disappointed me even more was that almost every response came from AI. I was never able to speak with a real support agent. The only explanation I received was that the transaction had already been executed, the conversion details had been displayed before confirmation, and therefore nothing could be reversed or refunded. I understand that Kraken may not have violated its own rules, and I acknowledge that the final amount I would receive was displayed before I confirmed the transaction. But I believe that “the information was displayed” does not necessarily mean “a new user truly understands what it means.” As a beginner, I saw what I believed was a normal stablecoin conversion. I had no idea that the number shown on the confirmation screen meant I was about to lose more than $800. If a platform expects users to recognize an $800+ pricing difference on their own, instead of proactively warning them that the conversion is unusually unfavorable, I don’t believe that’s a user-friendly experience—especially for beginners. I believe that a platform responsible for customers’ assets should do more than simply display numbers. It should also be designed to help users avoid obvious mistakes that can lead to significant financial losses. Crypto is already complicated enough. The risks users take should come from the market—not from a product design that makes such costly mistakes so easy to make. This experience has left me deeply disappointed and has almost completely destroyed my trust in Kraken. I’m not trying to deny Kraken’s rules, and I’m not asking for special treatment. I’m simply asking @kraken and @krakensupport to review my case, seriously consider improving the user experience, provide real human support when genuine mistakes happen, and consider a one-time resolution for customers who make an honest human error. If this could happen to me, it could happen to any newcomer entering the world of crypto. I hope my experience helps others avoid making the same mistake. Please double-check every click. @krakenfx @krakenpro @krakensupport

Spain takes action to secure its border after roughly 60,000 migrants crossed into its North African territory within 24 hours. Crews installed a 1,600-foot sea barrier between its North African territory of Ceuta and neighboring Morocco. The sudden arrival of migrants has sparked a humanitarian crisis and reignited the immigration debate in Europe.

Only if education could be this interactive ❤️‍🔥 I've had a looong wish to build something genuinely useful through vibe coding, and I finally did it. A 3D human anatomy application built with @threejs using GPT 5.6 Sol. It all started with a single design image that I created using GPT Image 2.0. I then used it to generate every 3D organ image, one by one. Next, I converted each of those images into 3D models using @tripoai (and no, they didn't sponsor this 😄). After that, I opened Codex, wrote a master prompt based on the design, and gave it the prompt, the design image, and all the 3D models. Codex built the first version beautifully, but there was one big problem. Every single 3D model was nearly 120-150 MB. That obviously wasn't practical for the web and was giving a performance of 16fps. After a few iterations, Codex optimized each model down to roughly 2–5.5 MB while preserving the visual quality, reducing the total asset size from ~900 MB to just 28.6 MB. And each model loads on demand. Along the way, Codex also generated those anatomical illustrations showing where each organ sits in the human body, and even created the interactive hotspot markers that explain different parts of every organ. It handled all of that. The process wasn't exactly one shot, but it also wasn't difficult. You just have to do it step by step. It genuinely felt like building something that could make learning anatomy much more engaging. The inspiration came from @DilumSanjaya's 3D animal plant cell project. I remember seeing it and thinking, "I want to build something like this one day." And I did it :D Live: Code:

#Polymarket’s# monthly user retention outperforms more than 85 percent of 275 sampled crypto projects, including DeFi protocols, wallets, and exchanges. While many platforms lose users after the first month, its cohorts show meaningfully higher rates of return trading.— Large early trades above the 90th percentile and at least $100 achieve only a 52.3 percent hit rate on final outcomes. That is a statistically detectable but economically modest edge over the roughly 50 percent baseline of all resolved trades. In the 2024 presidential markets, about 40 percent of traders participated exclusively in Trump contracts, and 71.8 percent touched Trump YES at least once. Only 0.7 percent of participants were active across more than two candidates, revealing extreme specialization rather than broad portfolio trading. — Kyle’s lambda in the same election market fell from roughly 0.518 in the early months to about 0.01 by October. As volume expanded, the market’s vulnerability to price impact declined by more than an order of magnitude. — Trading activity in the final quarter showed a clear intraday pattern, peaking between 09:00 and 20:00 UTC. The most frequent traders displayed even sharper concentration during U.S. market hours. — Across resolved markets since 2023, overall predictive accuracy on @Polymarket sits near 73 percent and rises to roughly 84 percent in markets exceeding $100,000 in volume. Only about 3.4 percent of markets faced formal disputes, most of them triggered by ambiguous wording rather than contested facts.

Josh Allen reunited today with a North Tonawanda teen at #Bills# camp who is a cancer survivor. She met Josh at Oishei and the family was emotional remembering his impact during an understandably rough time in their lives. #BillsMafia#