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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:

With dynamic workflows, Qwen3.8-Max programmatically plans tasks and orchestrates large-scale sub-agent systems, turning a single conversation into a fully automated, long-horizon task. Watch it showcase its full working ability!

A Japanese indie dev added full Czech language support to his Steam game after noticing a single person from the Czech Republic had wishlisted it The move earned tons of support from fans, and even Czech studio Warhorse who loved the dedication

Imagine driving a single route that takes you along China's entire land border and coastline. That is exactly what a massive new 27,000-kilometer loop will make possible. According to the Ministry of Transport, China plans to build this super-highway during the 15th Five-Year Plan period (2026–2030) by linking three major routes—the G219, G331, and G228—end to end. Tracing the country's borders and coastline, the journey will stretch more than half the length of the Earth's equator. Far more than a transportation corridor, the route weaves together hundreds of scenic areas and historic cities, offering a journey across some of China's most diverse landscapes. G219 runs about 10,000 km from the Kanas Scenic Area in northwest China’s Xinjiang to Dongxing in south China's Guangxi, crossing deserts, the Qinghai-Xizang Plateau, the Himalayas, and other spectacular terrain. G331 stretches about 9,200 km along China's northern border, taking travelers through the vast grasslands of Inner Mongolia, the fertile plains of northeast China, and iconic destinations including Fuyuan, Mohe, Changbai Mountain, and the Greater and Lesser Khingan Mountains. G228 extends about 7,400 km from Dongxing in Guangxi to Dandong in Liaoning Province, linking China's coastal provinces and dozens of seaside cities. Running across major rivers including the Pearl, Yangtze, Yellow, and Yalu, it offers sweeping views of the country's coastline. If you could explore one part of this epic loop, where would you start?

Okay, the @VulcanBench results for Qwen3.8-Max are in, and it is not what I expected. First, for anyone new to VulcanBench, here's a quick TL;DR on the eval suite: 23 frontier-hard software engineering tasks taken from real merged OSS PRs, run in a Docker sandbox, 3 runs per task across all three of its effort levels. No puzzles, no random abstract stuff, all real things engineering teams would do with these models. It looks like Qwen3.8-Max has a major overthinking problem, it uses a LOT of tokens and is very slow, period, no other way to see it. My cost to run this benchmark was $126.25, to run the exact same eval suite with DeepSeek V4-Flash was only $13.60. This makes Qwen3.8-Max an insanely expensive model. The tasks Qwen genuinely can't solve fail at every effort level, extra reasoning didn't help. The regression is almost all in work it already handles: six tasks that low solves every single time account for 83% of the 26-point drop, three of them collapsing to zero. It's not losing the hard problems. It's losing the ones it already knows how to do. Since Qwen3.8-Max hit a lot of wall clock budget caps, I thought I'd share more about this. - VulcanBench caps both steps (50–200) and wall clock (5–60 min), each scaled by repo size. - This is aligned with how comparable harnesses bound agents, DeepSWE caps rollouts at 100 environment steps, sitting right inside my step range; Terminal-Bench enforces a per-task wall clock; SWE-bench Verified scaffolds typically allow 20–60 min per instance with 250–350 step limits. - Every model on my chart gets the identical budget, and Qwen is the slowest model I've tested at 20–25 min/task. Soooo... Alibaba positions Qwen3.8-Max as trailing only Claude Fable 5. But on the kind of real coding work engineering teams would actually throw at it, under a fixed budget, its best setting lands mid-pack and its default lands last, so common. If you want to optimize for accuracy, Grok 4.5 is the move. If you want accuracy per dollar, DeepSeek V4-Flash is hard to beat, heck it's 10× cheaper than Qwen and you get higher accuracy. Qwen just isn't in the game at this point, this is not a model I could see engineering teams using for daily coding work.

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