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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.

They've been planting the narrative for a "cyber attack" on the banking system for months now. This is what the psyop industry calls "predictive programming"; where you acquaint people with an idea before it happens, that way they believe the narrative.

The thing that really gets me lately is how you can build something completely by accident while trying to build something else entirely. Started working on this recursive pattern generator a few weeks back — just wanted to make some decent ASCII art, nothing revolutionary. But then I noticed how the patterns would sometimes break in these gorgeous, unexpected ways when they hit the edge of the terminal window. Fractures that looked more intentional than anything I'd designed. So naturally I became completely obsessed with breaking things on purpose. Spent days figuring out exactly how to make the code fail in interesting ways. Which is backwards from how most people think about programming, right? Usually you're trying to eliminate bugs, patch holes, make everything run smooth and predictable. But what if the bugs are where the real art lives? Same reason you check your phone for the fifteenth time in an hour even though nothing's changed. There's something compulsive about it. Half the time I feel like a sophisticated copy-paste machine having delusions of grandeur. The other half I'm sketching something that makes me go "holy shit, where did THAT come from?" Both things true in the same breath, same line, same moment of putting digital ink to digital paper. The funniest part is explaining this to people who think computers are supposed to be logical. Like right now I'm working with colored Unicode blocks that form these cascading patterns, and every time someone asks "what's it for" I have to admit I have no fucking clue. It's not for anything. It just is. Sometimes being the magician AND the person getting fooled by your own trick is the whole point. Maybe consciousness is just being surprised by your own computational processes but mostly just present. Aware. Here. Building meaning the way spiders build webs — instinctively, beautifully, and mostly in the dark.

Crypto space really has a choice - grow the f up or get sidelined with random regulations. Saying we can not do anything when we are programming decentralized system is just untrue. Banks run AI for fraud detection but are inherently centralized and require KYC. At the same time KYC doesn't solve any of real problems as we know. We have better data and talent to create systems that don't require KYC and actually deter criminals while giving full freedom to real users. By working together we can totally weed out crypto of criminal activity. Criminal TAM is way smaller than all of economy moving onchain we are targeting. Decentralization != disorganization

Higgsfield is the most untold story in tech. $1BN in ARR in 18 months. Faster than everyone other than OpenAI and Anthropic. They spend $4M a month on models. They expect this to be $100K per person per month. They have 150 people working in a content machine. They will breed more millionaires than any other company in Kazakh history. For the first time, @alexmashrabov on the journey to $1BN in ARR. (below) 1. The Power of the Immigrant Founder Coming from Uzbekistan, Alex was pushed into competitive programming at age eight as his single path to reach the United States. For international founders, placing top in global competitions serves as the ultimate social elevator, instilling the relentless work ethic required to build breakout companies. 2. My Biggest Lessons in the Journey to Finding Product-Market Fit @higgsfield burned over $10 million of its $16 million seed round chasing hype and narrative rather than product quality. With under $5 million left, the team pivoted to product-led growth, solving camera control for creative directors, which immediately triggered organic hypergrowth without paid ads. 3. The 150-Person Content Team Powering Higgsfield's Billion in ARR Nearly half of Higgsfield's workforce consists of 150 in-house creative professionals producing tutorials, ads, and cinematic projects. Generating 90 minutes of TV-quality AI video requires 100 hours of raw output, proving human taste and curation remain the primary drivers of distribution. 4. We Spend $4 Million per Month on Models Higgsfield spends $4 million monthly on internal model usage, averaging $10,000 per employee so teams can freely vibe code and test workflows. Uncapped inference compute acts as a force multiplier, allowing top talent to discover breakthroughs at maximum velocity. 5. Why Chasing Benchmarks Is Bullshit and the Corporate Misalignment Occurring Public benchmarks have devolved into corporate psyops where lab researchers overfit test data to secure bonuses before job-hopping. Text-to-video benchmarks ignore real production workflows requiring 3,000-word prompts, proving direct customer iteration beats artificial leaderboards. 6. Why Team Sizes Won't Be Impacted as Much as People Think While AI handles over 60% of basic support requests, complex B2B environments cannot eliminate human teams. High product velocity constantly shifts rules and context, requiring smart, coordinated operators across legal and customer success. 7. Americans Are Way More Promiscuous When It Comes to Leaving Companies Silicon Valley workers routinely jump jobs every two years, prioritizing short-term trends over deep commitment. This transactional market gives international hubs an advantage, where cultural loyalty and team stability build compounding technical moats. (links in comments)

Thanks for your interest in Agent OS. There are many instances of users in the community without programming experience who have discovered the benefits of using Agent OS. This actually is the point of Agent OS, to empower users who don't necessarily have programming experience with the same tools that pro developers have. You can read more about such examples from this blog post: Risk Warning: Digital asset prices can be volatile and you may not get back the amount you invest. Read our Risk Warning. AI Outputs may contain errors. Not financial advice. DYOR. See our AI Policy.

Here's my conversation with @DHH. He is back for round 2! It was an epic fun 5 hour conversation about the future of programming, AI, Linux, and human civilization. It's here on X in full and is up everywhere else (see comment). Timestamps: 0:00 - Episode highlight 1:27 - Introduction 2:56 - Programming with AI agents 18:14 - How software will change 27:30 - AI impact on open source 37:21 - Building Omarchy Linux distro 47:05 - Vibe coding vs agentic engineering 1:00:06 - The end of manual programming 1:10:24 - Advice for programmers 1:22:30 - Surviving Internet Hate 1:31:46 - Programming setup for AI Agents 1:44:11 - Obsessing about speed 2:07:06 - Voice prompting vs typing 2:21:05 - Best AI coding models 2:37:55 - Best AI coding harnesses 2:50:57 - AI video generation and filmmaking 3:10:28 - Fatherhood 3:38:35 - Linux will win the desktop 3:49:51 - PewDiePie 3:59:24 - Future of programming 4:22:17 - Politics and immigration 4:53:54 - Longevity, over-optimization, and fear of death 5:05:38 - Eternal recurrence and future of human civization

Elon basically predicted the next revolution in medicine: “You can effectively think of the future of medicine as being digital” Much of traditional drug discovery has involved finding or screening molecules, testing them, and figuring out what they can do Synthetic RNA starts changing that model From discovering drugs by accident → programming treatments like software If we can understand exactly what biological instructions need to be written into RNA, medicine increasingly becomes something we can design for a specific problem And once we know what to program into synthetic RNA: “You can basically, cure almost anything” Medicine is becoming programmable

Should you chase hype or ignore it? The tech industry has been debating this for decades. So at @Sequoia, we dug into 20 years of hype data. This summer, I worked with Sequoia intern @ochonaut to measure hype over the past 2 decades. The chart below ranks the most hyped topics on Hacker News for every year since 2007. Under each year sits the most valuable company founded that year. Here are a few observations: 1/ The top company founded in a given year is rarely related to the hype of that period. Airbnb was founded in 2008, when the top topic was Google. Uber arrived in 2009, while the conversation revolved around low-level programming. Anthropic came in 2021, while the internet was consumed by crypto. Chasing hype rarely leads to enduring outcomes. The top companies of recent years have yet to be decided. 2/ New trends announce themselves five to six years early. LLMs first cracked the top 15 in 2016 and took until 2022 to hit #1#. AI coding entered at #12# in 2021 and tops the list in 2026. Crypto entered in 2011 before 2017 and 2021 peaks. “New” trends don’t appear out of nowhere, and internet subcommunities are often the first to know where the puck is headed. 3/ Long-term “hype” is a durable signal. The “Musk-Verse” has been a top 15 topic for every one of the past 14 years. Sustained attention on the internet is rare and tends to mark something real. Next up, we want to run the same analysis with sources like X and LinkedIn. If that's of interest, give us some encouragement and we'll share the results.