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／ #AveMujica# アモーリス シグネチャードラムスティック #LERNI# より 4月26日 発売‼️ ＼ お求めは全国の楽器店にて。 4月26日、27日にKアリーナで行われる MyGO!!!!!×Ave Mujica 合同ライブ「わかれ道の、その先へ」 の物販ブースでも販売いたします❗️ 是非お手に取ってください🌙 #バンドリ#

I’ve decided to leave NVIDIA. There are a few things I want to say. As a robotics engineer at NVIDIA, I have seen Jensen as much more than the company’s CEO. I admire his willingness to invest in a future that was not yet obvious. He committed to CUDA long before the deep learning boom and positioned NVIDIA for AI before it became everyone’s priority. Those decisions helped build the infrastructure that powers today’s AI. I have seen that same vision in physical AI: investing in robotics, sharing open-source tools and projects, and building computing platforms that bring AI into the physical world. This is the work I do every day—and a future I believe in. But what matters most to me is the culture he helped build. Engineers are encouraged to explore, take risks, and learn from mistakes. Jensen reads employees’ Top 5 emails, follows their work, and engages directly. In a company this large, that connection matters. It makes an individual engineer feel that their ideas can still be heard. That is why this has been so disappointing. I do not have all the facts surrounding the allegations, and I cannot prejudge the investigation. My admiration for Jensen does not put him above scrutiny. What I find difficult to accept is having to come to terms with his departure before receiving an explanation that helps me understand it. What this means for our direction, our culture, and the people doing the work remains unclear to me. I have felt that engineers’ perspectives matter here. On this decision, I have not felt heard. That gap has been more painful than I expected. I understand that some information cannot be shared immediately. But that does not erase the feeling of being left outside a decision that affects the company we help build every day. A company’s greatness comes from its leadership, its governance, and the people who turn its ambitions into reality. Employees’ trust and sense of belonging are part of that, too. Jensen’s vision and the engineering culture he shaped were important reasons I wanted to stay. His departure led me to decide that my own chapter here should also come to a close. I leave with sadness and gratitude. The colleagues I have worked with, and the work we believed in together, will always matter to me. These are my personal views.

【NVIDIA NeMo Switchyard在Amazon Bedrock上为编码智能体请求分配合适模型】 AWS机器学习博客11月13日发文，6位AWS和NVIDIA作者介绍在Amazon Bedrock上为编码智能体做模型路由。 作者主张用每个解决任务的成本衡量路由，而不是每个token的单价。 示例把开源网关LiteLLM和Switchyard插件部署在Amazon ECS Fargate的私有子网里。 LiteLLM通过Bedrock Converse API调用模型，流量经过VPC接口端点进入Bedrock。 能力较强的模型使用Claude Opus 5.5，高效模型使用NVIDIA Nemotron 3 Super 120B。 Stage策略读取近期工具结果在两个模型之间选择，信号不明确的时候默认使用高效模型。 Random策略按照权重分配流量，适合A/B测试，两种策略都不调用评判模型。 作者表示只在两个任务上跑了几次，不足以得出成本或者质量结论，示例只适合非生产账户。 我觉得现在只给了部署模板，团队需要自己用bench脚本量出每个任务的实际成本。 来源：AWS Machine Learning Blog · 2026-11-14

ChatGPT 里有个不太起眼的功能叫 Learning Mode，很多人没注意到。 用它学东西，可以像请了个一对一私教：从基础讲起，一步步往深走，不会一上来就被信息量砸晕。 下面 5 段提示词，能把它的教学模式调出来：

Be Jensen: > Asian American Dishwasher at Dennys > Founded $NVDA and pioneered GPU computing > Took an extraordinary gamble in CUDA, despite the company close to bankruptcy risk. > Bet paid off heavily, and Nvidia was there as the infrastructure for deep learning and generative AI. > Spent last 33 years of his life growing $NVDA, and finally saw it become a $5T+ company > Devoted his life to establishing American AI leadership > Started having more influence on policies that Washington conflicted with > Leaks and hit pieces happen all at once around midterms > Washington tries to push out others like Lutnick that shared similar China stances to Jensen. > Forced to resign by Taco Bell and NFL experience board members over unconfirmed Spy accusations + personal relationships. > Huang's leadership then questioned by politicians that ask how $META makes money or how Wifi works. > Slandered by US media that think because Mcdonalds did well under a CEO change, so can American semiconductors like $NVDA. This feels like a horrible joke. Jensen Huang was the pinnacle of visionary tech CEOs and living the American dream. Ousting Jensen Huang will go down as one of most consequential and darkest decisions in recent US history.

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Everyone believes CUDA is NVIDIA's moat, the reason we've maintained our dominance in the AI era. I've never believed that. NVIDIA has only one true moat: its people. And the most important of them is Jensen Huang. I've worked on CUDA compilers, libraries, and DSLs for eight years, from Volta to Rubin. I think I've earned the right to share my perspective on what NVIDIA's moat really is. I hope you'll bear with me. 1. What is CUDA? Many people think CUDA is just a programming language and its compiler, and that NVIDIA's moat comes from the massive amount of software written in CUDA, making it impossible for developers to leave NVIDIA GPUs. This is completely wrong. It's an incredibly naive understanding of CUDA. To me, CUDA is everything NVIDIA does. CUDA isn't just a language, software, or hardware architecture. It's a product defined by a unique customer experience. When you find GPU programming too difficult, CUDA gives you an easier way. When your GPU code isn't fast enough, NVIDIA engineers respond and help you solve it. When a handful of GPUs can no longer push your technology forward, NVIDIA has already scaled its computing platform from desktops to massive data centers. When you need more data centers than anyone else can supply, NVIDIA has already secured wafer and HBM capacity years in advance to meet your demand. That experience is CUDA. It's made possible by the relentless, day-to-day work of everyone at NVIDIA. And the person who brings all these people together, who gives them a shared vision and purpose, is Jensen Huang. 2. What made CUDA possible? Patience. Perseverance. An unwavering commitment to a vision. Around 2006, NVIDIA bet that the future would demand far more computing power, and began transforming GPUs from graphics processors into a platform for general-purpose computing. Initially, it was a commercial disaster. The added costs hurt margins, the market wasn't ready, and the strategy faced enormous skepticism and pressure, including questions from the board. But Jensen persisted. For roughly a decade, NVIDIA kept investing in CUDA, building its hardware, software, and developer ecosystem long before the commercial payoff became clear. Eventually, deep learning arrived. And NVIDIA was ready. Today, this sounds like an inspiring story. But for those who lived through it, it meant a decade of perseverance, doubt, and being misunderstood. Looking back at the history of technology, how many people could have done the same? This is what I've always loved most about NVIDIA. And now, Jensen has been pushed out. This won't kill NVIDIA. The company can still create tremendous commercial value. But it has killed NVIDIA's soul. And how far can a body go without its soul? If this is how Jensen Huang is treated today, how will those of us who built CUDA be treated tomorrow? I love NVIDIA deeply, but I have to say it: NVIDIA is dead.

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Hallo! Meine chinesischen Freunde! Ich habe eine Frage an alle. Ich weiß, dass X in den chinesischen Anacra Einschränkungen unterliegt, es ist oft notwendig, ein VPN zu verwenden, um darauf zuzugreifen. Was treibt Sie also trotz dieser Einschränkungen dazu an, X zu verwenden? Grundsätzlich Freunde aus anderen Ländern finden, internationale Nachrichten verfolgen, neue Dinge lernen, verschiedene Diskussionen im Internet verfolgen oder ein zusätzliches Einkommen verdienen?