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Yes, the green flame is caused by burning copper from depleting fuel in the regenerative cooling circuit and going oxygen-rich (closer to stoichiometric temperature) in the main combustion chamber. Slight underperformance of engines on this flight is the reason. The booster is meant to land with tiny residuals, so the solution is to improve engine performance and performance consistency (underway with Raptor V3.2), as well as adjust flight software to do MECO1 about one second earlier to ensure enough propellant for boostback and landing (tiny change). These early ships and boosters are already outdated by improved designs in production or on the test stand, so this booster loss does not change our timeline to reach high volume, fully reusable Starship flights.

很多人翻墙之后，只会刷推特 其实真正的信息差，藏在海外这些网站里。 下面这 10 个地方，建议全部收藏。每天花半小时逛一圈，信息获取能力可能超过身边 90% 的人！ 1、Reddit 互联网最大的兴趣社区之一。AI、投资、编程、创业、科技，几乎任何领域都有专属 Subreddit。真人经验、深度讨论、行业内幕，挖宝圣地！ 2、GitHub 全球最大的开源代码社区之一。AI Agent、LLM、大模型、自动化工具、开发框架，想找宝藏开源项目，这里简直挖不完！ 3、Hacker News 硅谷程序员的情报站。AI、开源项目、技术趋势、创业故事，每天都有高质量讨论，技术信息密度爆表！ 4、Product Hunt AI 工具和 SaaS 产品的发射场。每天都有新产品上线，很多爆火的 AI 项目都能在这里提前发现。 5、LinkedIn 千万别只把它当招聘网站。全球 CEO、投资人、创业者、科技公司高管都在这里分享一手行业动态。 6、Telegram 海外信息集散中心。AI、加密货币、投资、开源工具，各种频道和社群层出不穷，不少一手消息传播速度非常快。 7、Threads Meta 旗下社交平台。海外创作者、开发者、创业者聚集地，氛围比 X 更轻松，经常能看到有意思的新观点。 8、Discord 海外互联网社区的大本营。AI 项目、Web3、游戏、开发者社区都喜欢在这里建群，很多项目的核心讨论和早期动态都在 Discord。 9、Medium 海外技术和商业长文聚集地。AI 技术教程、创业复盘、投资思考、产品分析，适合系统性补充知识。 10、Substack 独立作者和行业专家的内容订阅平台。大量投资人、科技创业者、研究员在这里发布深度报告，有些内容比新闻媒体更有洞察力。 最后说一句： 不要把翻墙后的互联网，只当成一个刷 X 的娱乐工具。 Reddit 找讨论，GitHub 挖项目，Hacker News 看趋势，Product Hunt 找工具，Substack 追踪大佬观点。 这才是打开海外互联网的正确姿势！ 建议收藏，也转发给还在只会刷 X 的朋友。 你还有哪些值得推荐的海外网站？评论区补充！

Thinking about joining BNB Bangkok, but trying to figure out where you fit? Whether you have an idea, a live product or just want to attend Demo Night, here’s what you need to know before Nov 25 - 27 🧵 👇

用了 AI，感觉干活快了，实际却更慢了？ METR 的一项研究出现了这个反差：16 名资深开源开发者完成 246 项真实任务，每项任务随机决定是否允许使用 AI。 结果是，使用当时的 AI 工具，完成时间增加了约 19%。但实验结束后，开发者仍估计 AI 帮自己节省了约 20% 的时间。 推荐读这篇《Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity》。它提醒我们：初稿出来得快，不代表任务完成得快。等待生成、检查结果、修改返工，都要算进总时间。 不过，别把它概括成“AI 让程序员变慢”。研究针对熟悉成熟项目的开发者，使用的是 2025 年初的工具，不能代表所有编程工作，更不能直接代表今天。 还有个值得一起读的后续：METR 在 2026 年更新中观察到一些加速信号，但也指出参与者选择和计时方式等问题，使新数据难以可靠估计提效幅度。 比起争论 AI 到底提不提效，更实用的办法是：选一项重复任务，记下从开始到真正交付的总用时，再看质量和返工有没有变化。 “用起来很顺”和“确实省了时间”，可以分别记录。

A quick look around HyperHive. Five roles, one crew, built on @HyperliquidX's public API. AI writes the card, code sizes it, a sealed key trades it, and every fill is matched to a signed slip. Product preview for now. Trading perps can lose money.

An American porn actress says a single shoot in Madrid involved 80 men and nearly a liter of semen. She counted 122 ejaculations, followed by another 10 onto her face. The night before, she had sat across from the director asking about pay and medical testing. He answered by opening a spreadsheet. In the interview below, she describes how the production was organized, from recruiting performers to deciding when she could take a break. According to her, the studio maintained a roster of around 300 men in Madrid. Getting onto it required a referral. Test results fed into the spreadsheet so the crew could track who was cleared to participate. The men could earn more depending on the shot they delivered. She says she was given three medications before filming, including one she describes as a muscle relaxant. Nine women worked as fluffers. Their waiting room had pink furniture and candy on the table. Outside, the men lined up while her videos played on a television. The shoot followed a repeating schedule: ten ejaculations into her mouth, swallow, then another ten. After fifty, she took a break and recorded an interview before returning. One assistant held a bowl beneath her chin to catch whatever spilled. She says the role even had a name: “scooper.” Amid all that coordination, she remembers one man missing and quietly apologizing. “I’m so sorry. I’m so sorry.” For the final sequence, she requested ten men instead of the usual five. Then the director addressed the group in Spanish. One word caught her attention: “ojos.” Eyes. Afterward, she asked whether he had instructed them to aim at her eyes. He denied it. According to her, he had repeated something she mentioned over dinner - that she didn’t mind and the men interpreted it differently. She has since referred other American performers to the production. “I’ve sent them so many Americans.”

GCSA Insight: Critical Security Vulnerability Discovered in a Leading Robotics Manufacturer’s Product, with System-Level Privilege Compromise Verified on Physical Hardware As artificial intelligence and embodied AI technologies continue to advance, robots are evolving from conventional automated machines into intelligent systems capable of perception, decision-making, and autonomous operation. At the same time, cybersecurity risks are extending beyond software and data into the physical world. The security of robotic systems is no longer solely a matter of protecting data and devices; it can also directly affect operational safety, physical environments, and user safety. Recently, the security research team of the Global Cybersecurity Alliance (GCSA) discovered and verified a critical security vulnerability in a product developed by a leading robotics manufacturer. Through in-depth analysis and multiple rounds of testing on physical hardware, the team successfully breached the device’s system security boundaries and obtained the highest level of operating system privileges in a real-world device environment, demonstrating the vulnerability’s actual impact under specific testing conditions. This research encompassed the entire process, from identifying the security flaw and assessing its risks to validating its impact on physical hardware. The findings also highlight significant challenges facing the underlying security mechanisms of embodied AI systems. The research findings and supporting evidence have been formally submitted to the manufacturer. On November 13, 2026, the manufacturer responded by email, preliminarily confirming the existence of the vulnerability and stating that it had initiated internal remediation efforts and an assessment of the scope of affected products. It is important to emphasize that confirmation of a vulnerability does not mean the associated security risks have been eliminated. GCSA will continue to monitor the manufacturer’s assessment of the affected scope, remediation plans, and subsequent security validation, and will update its research findings as new information becomes available. For intelligent systems capable of physical interaction and movement, timely, effective, and verifiable security remediation is particularly important. In accordance with responsible vulnerability disclosure principles, GCSA is currently withholding sensitive information, including the manufacturer’s identity, specific product details, the vulnerability’s underlying mechanisms, and exploitation methods. GCSA is currently seeking guidance from relevant authorities and regulatory bodies regarding the associated security risks. Taking into account the manufacturer’s remediation progress and a comprehensive assessment of potential security implications, GCSA will carefully determine the appropriate arrangements for further disclosure. Subject to appropriate conditions and upon obtaining the consent of the relevant authorities and regulatory bodies, GCSA will publicly release a detailed security insight report, providing the industry with ongoing updates on the research findings and associated risk assessments. The findings of this research are based on specific testing environments and the evidence obtained to date. They do not imply that other product models or similar robotic systems are affected by the same security issue. This security research and validation on physical hardware represent one of GCSA’s initiatives in the field of embodied AI security. GCSA will continue to advance security research, coordinated vulnerability disclosure, and industry collaboration to promote stronger security response mechanisms across the artificial intelligence sector, enhance product security, and strengthen industry trust. 🔗 Read the full report: · Stay Connected with GCSA Follow GCSA's official social media channels for the latest news, product launches, research developments, and ecosystem updates. GCSA Official Telegram Channel: GCSA Official WeChat Account: #GCSAInsight# #GCSACybersecurity# #GCSASecVibe#

Woman: "Hey, can you lower your laptop volume? The entire café can hear your Zoom meeting." Tech Bro: "Sorry, I’m leading a product sprint. It’ll only be another hour." Woman: "There are people trying to study and read here. Why not use earbuds?" Tech Bro: "Earbuds hurt my ears. Honestly, it's a public coffee shop, deal with it." He smirked, sipped his iced latte, and unmuted his mic to address his team. Tech Bro: "Alright team, let's review the Q4 lay-off list before HR finalizes it." The woman across the table calmly closed her book, walked over, and pulled up a chair right next to him.

new in Tolt: referral widget. bringing referrals inside your app just got a lot easier. give every signed-in user their own referral link and let them track what they earn, without leaving your product. @blitzapi is already putting it to work.

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.