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⚠️ Cursor has published a security incident report on the earlier supply chain attack. According to the investigation, the attacker used access keys issued by Cursor to Fireworks and subsequently exposed on Fireworks’ side to gain access to some of Cursor’s SaaS services and internal systems. The attacker ultimately tampered with the distribution cache for the Claude Code extension to collect information about users’ machines and install SSH backdoors. The compromised versions were Anthropic.claude-code 2.1.312 and 2.1.313 in Cursor’s VS Code extension marketplace distribution cache, affecting macOS and Windows. The exposure window ran from October 29 at 17:27 to October 30 at 04:35 (UTC+8), with up to approximately 5,000 users affected. Security recommendation: If you installed or updated to either affected version through Cursor’s marketplace during this window, read the official notice as soon as possible and run the remediation script for your platform to check for and remediate any compromise: Fireworks’ initial announcement and subsequent update:

not all crypto rewards are created equal. when a platform advertises yield, i think the first thing to check is what holders actually receive and where that value comes from. @RebateLaunch has an interesting approach to this. instead of paying holder rewards in newly issued $rebate, eligible holders receive payouts in the pair asset. on robinhood chain, that's usdg. the platform reports: → $28.5k distributed to holders. → 120,403 individual payouts. → rewards sent several times throughout the day. → no staking or manual claiming required. but the reward distribution is only one part of the model. rebate pad also uses a declining launch tax to address early sniping. → 99% during the initial stage. → 66% in the next stage. → 33% in the following stage. these penalties go into a locked treasury that supports the buyer bonus mechanism. separately, the 0.7% platform fee is used to buy $rebate from the market and burn it. the reported cumulative burn has reached 35.3m tokens, representing 3.53% of the original 1 billion supply. what makes this model worth examining is how several mechanisms connect: ✦ early launch activity contributes to the treasury. ✦ the treasury supports buyer bonuses. ✦ trading fees fund holder payouts and the $rebate buyback-and-burn mechanism. my perspective is that paying rewards in the pair asset is an interesting distinction because holders aren't simply receiving more of the same token. however, attractive payout numbers alone don't prove sustainable yield. trading volume, fee generation, treasury health and long-term user activity still matter. i'd rather evaluate the source of rewards than focus only on the advertised percentage. also, round 3 of the creator campaign closes november 17 at 22:00 utc. the real question is whether rebate pad can maintain this economic cycle as adoption grows. $rebate

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#

My husband stopped initiating sex after I turned him down three times in one week. At first, I thought he was finally understanding that I couldn’t always be in the mood whenever he was. Then I noticed he had stopped touching me altogether. No random kisses in the kitchen. No pulling me closer when we watched television. No morning hugs before leaving for work. I actually preferred it that way until one evening I asked why he was behaving like a stranger. He looked at me and said, “I figured you wanted me to stop making advances.” I laughed. “There’s a difference between not wanting sex and not wanting you to touch me.” He nodded slowly. “That’s the part I’m struggling to understand. Because lately, every time I reach for you, I have to calculate whether you’re going to feel loved or pressured.”

Gue gatau lagi harus cerita kemana. Jadi gue udah pacaran 13 tahun, ga pernah putus nyambung tapi detik ini gue minta break sebulan sama cowok gue. Tebak karena apa? Bukan karena selingkuh, tapi karena kemaren pagi gue nemuin sesuatu di gemini ai nya. Guess what?

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.

I am actually disgusted by the timing of this drop. And this will get a bit more technical. Let’s do a real technical teardown of this "1,385-sigma Blackwell weight fingerprint" report that OpenAI and Anthropic are breathlessly promoting today, because banking on nobody reading past the headline is how DC narratives get manufactured. ​First, look at the actual CUDA maths: when PyTorch initialises model weights (torch.nn.init) with a fixed PRNG seed, curand/Philox parallelises random number generation across the GPU’s Streaming Multiprocessors (SMs). Because Hopper has 132 SMs and Blackwell has 148 or 152 SMs, the exact same seed lays out initial weights in a different spatial pattern depending on the GPU’s SM count. In huge MoE models, certain low-gradient null-spaces or unrouted slices preserve faint directional traces of those step-0 weights even after training. ​So yes, brute-forcing the seed and SM grid can show that a model’s step_0 weight initialisation script ran on a 148/152-SM Blackwell GPU. Now read what the summary explicitly admits in the fine print: 1. Initialisation is not full training: The report literally states that these signatures show how the weights were created, and "support (but do not prove) the inference that Blackwell also participated in model training". Initialising a model checkpoint takes seconds on a single rented dev node before loading the safetensors onto a standard Hopper cluster. The "2,000 to 3,000 Blackwell GPUs" and "$500 million" cluster numbers do not come from the weight fingerprint and Depue admits in his own thread that he simply "estimated" cluster sizes from release schedules and model details. ​2. Zero geolocation and zero proof of smuggling: The final sentence of the executive summary explicitly admits that the findings "do not establish the physical location of the chips, how they were acquired, or whether export control regulations were violated". Depue himself rented a cloud GB200 instance just to run his verification test. Commercial labs that are not on the Entity List renting bare-metal Blackwell nodes in offshore clouds (Singapore, Japan, or the Middle East) is completely legal under BIS rules and leaves the exact same SM fingerprint. Now ask yourself why a former OpenAI researcher dropped this on November 12, the exact day Jensen Huang was pushed out, with OpenAI and Anthropic immediately stepping in to "independently verify" it. US closed-source labs are watching their paid API moats get eaten alive by free Chinese open-weights models. Conflating a step-0 PRNG initialisation seed with a "$500M smuggled Blackwell cluster" hands DC hawks the exact pretext OpenAI and Anthropic want to regulate global cloud compute and kneecap open-source AI.