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Meet the Gemma Translator! A fully offline device powered by Gemma 4 E2B built with @Antigravity. Running entirely on a Raspberry Pi 5 with a connected microphone and speaker, this highly portable prototype is housed inside a custom, 3D-printed case.

10. 蟹肉生菜杯 • ~190卡路里 | ~20克蛋白质 材料： • 4盎司蟹肉（罐装或新鲜） • 1/4个鳄梨，切粒 • 切片黄瓜 • 柠檬汁+辣椒酱 • 罗马生菜或黄油生菜杯 蟹肉是海鲜中PCR最高的之一。 鳄梨添加健康脂肪完善餐点但不会高卡路里。

Tomorrow will be my last day at Google after 27 years, and watching it grow from 25 people to 190,000+ has been an amazing journey. Below is a note I shared with many people internally at Google today. An excerpt is: It has been an absolute pleasure to work with you and to help build some of the most widely used and impactful products of all time. As a kid, I dreamed of helping build software that would be used by many people, and Google now has thirteen products used by more than a billion people (amazing!). Our work has had a tremendous impact in the world, and I have been lucky enough to collaborate and form friendships with many colleagues that I deeply admire, respect, and enjoy. It still brings me joy every time I see people out in the world using our products to find information, handle email, translate documents, watch videos, learn new things, navigate and understand the physical world, browse the web, use their phone, run large-scale computations on our infrastructure, ride in an autonomous vehicle, or perform complex tasks with the help of our AI systems. I hope you all share this sense of joy, because it is a shared accomplishment! Thank you to all of my colleagues at Google over many years! Now I'm excited to go start @DiscoLoopAI with my longtime friends and colleagues @Sanjay_Ghemawat, @OriolVinyalsML, and @quocleix. (Updated post: slightly redacted to not have some personal info)

关于护照 之前我和好朋友 @nutsh33 去办护照 他的申请表上填了意向国家是日本 柜台的警官收进去以后没说什么，在意向国家那里划了一下，改成了英国 然后说了句「和你一起来的人我见过，所以我知道你是真需要护照 只是你填了日本温州市那一级肯定不会给过，所以帮你改了」 看来还是有愿意让我们好好生活的人

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.

好久都没看到什么新的有意思的产品了，天天看到的就是，又有一堆人出了一个Agent，然后用一下发现一坨屎，卸载，然后另一堆人出了另一个Agent，用一下发现又是一坨屎，在卸载。然后在电脑里拉的.xxxx文件夹的💩还得手动清理。。。真就没啥让人耳目一新的玩意儿。。。。

美股盘前光通信股盘前已经彻底疯了 Coherent涨超15% Lumentum涨近12% 康宁涨超9% Credo涨近10% 迈威尔科技涨近8% 阿懂又整事儿……明儿大A光模块有的受了……

最近的中国天宫系列 AI 视频非常火，基本都是mj垫图+seedance/kling生成。 但不是所有天宫作品都具备高级感，我发现跑出圈的这些不只是因为场景宏大，还有很多构图/色彩技巧。 构图上，用树木、岩壁和廊柱形成天然画框；用极小的人物对比巨型建筑与云海；中轴、对角线和S形动线交替使用，让每个镜头既稳定又有纵深。 配色则非常克制：大面积低饱和蓝灰、象牙白打底，只用少量朱红和橙金点亮视觉中心。远景继续降饱和，前景加深对比，空间感自然就出来了。