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Three months into the Iran war, the oil market is coming to grips with an unexpected new reality: China, the world's largest importer, needs much less fuel than previously thought

@Shilllin One of the most important lessons of product management: What your users say they want is often very different what they actually want The author of the post above is in the top 1% of React-with-Video users. Since making that post, his volume has only accelerated.

House rule, now official: The protocols that go viral become free. The ones behind the door belong to members. Three weeks ago this was an idea with a logo. Then 200M+ of you showed up: LATAM, Vietnam, Japan, the US and two protocols became famous: Pattern Audit and Drift Forensics. Famous things belong to everyone. Some of you found the door this week and hit a password. You hated the password two months ago, too. You were right both times. As of tonight, there isn't one: no password, no account, no email. The door is gone. ECHO Premium Lite: Audit + Drift, free. In your browser. Unlimited prompts. Not a demo of the tools, the actual tools. The same door is on every one of our pages: US, LATAM, Asia - pick yours. The console is in English, the protocols don't care. Translate it with the same AI, paste, audit. 🌊 Open it. Paste a conversation. Audit it. 🌊 Para LATAM — ustedes llegaron primero. Sin cuenta, sin correo. Tradúcelo, pégalo, audita tu conversación. 🌊 Gửi Việt Nam — cảm ơn vì đã luôn đồng hành. Không cần tài khoản. Dịch · dán · kiểm tra hội thoại của bạn. 🌊 日本のみなさんへ — いつもありがとうございます。アカウント不要。 翻訳して、貼り付けて、会話を監査。 And the other eight protocols? Sealed, behind the member door. $9.99/month. One new protocol drops every month, included. When one of them goes viral, it goes free: that's the rule. Members just live in the future. One more thing: you decide what goes free next. Which of the eight sealed protocols should earn its freedom? The comments pick. As always: Build carefully. Question assumptions. Verify important decisions. And don't let drift compound. P.S. entrepreneurs and job hunters: something is coming for your side of the table😉

$AMD| The FOMO to buy @AMD Chips is NOW 🧵 Not Financial Advice! DYOR! Research Purpose Only! The Inference Queen is the biggest winner in Agentic AI where all other CPUs are struggling to compete with a 2yr old EPYC Turin and EPYC Venice is in mass production phase. AMD stresses deployability today on standard x86 platforms (no proprietary architectures required), full software compatibility, and open standards. This positions Venice + Helios as a practical, high-density alternative to competing solutions while underscoring that agentic AI shifts the balance toward CPU-rich racks alongside GPUs, and most importantly, lowering the cost of token to accelerate adoption and innovation. Context: @WSJ yesterday came out with an article that @OpenAI is condiering drasstically lowering the token prices to win more customers from Anthropic. The narrative "they" are trying to exacerbate the current AI selloff won't last long. This is a fundamental misunderstanding of what is going on, or what I already discussed for months and years. Followers and Subscribers already knew this for years, that this day would come, where token cost will bcome the central discussion among enterprises as there is no such thing as unlimited budget or Tokenmaxxing when they use $NVDA chips or In-house Hyperscalers chips. I will link various threads if you are interested in understanding the full picture from supply chain to recent TSMC Rapid 2nm expansion up to 12 Fabs total by 2027/2028. Hyperscalers and AI natives effectively have no choice but to buy more AMD system for Agentic AI as leadership in economical, power-aware, high-volume internal + agentic use. However, due to supply constraints where Supply is far behind Demand, this makes multi-vendor reality along with in-house chips drive faster industry progress, lower overall costs, and better sustainability. NVIDIA’s Vera Rubin cannot compete with a 2 years old EPYC Turin, but AMD under Dr. Lisa Su has engineered the lowest cost-per-million-tokens, highly competitive energy-efficient solutions, and superior CPU orchestration for agentic AI at scale with Helios. Dr. Su has championed this shift since at least 2023, foreseeing the rise of agentic workflows that demand far more orchestration, parallel agents, and balanced compute well before the industry fully embraced it. Her long-term vision of AI moving from simple prompts to always on, multi-agent systems has driven AMD’s investments in high-core EPYC CPUs and integrated rack-scale solutions, perfectly positioning the company for today’s realities. The OpenAI-AMD 1GW Helios deployment (starting H2 2026) represents a pivotal vertical integration move that directly supercharges the inference economics. This isn't incremental; it's a structural shift toward ownership of massive, optimized rack-scale capacity, enabling the lowest token costs and triggering the enterprise adoption flywheel. We need to be honest, $AMD is the only company that made a big bet on Inference since the day Chatgpt became sensational where $NVDA and others were betting big on Training. At the end of the day, Token bill from @AnthropicAI has to obey economics. Meaning the bills rise, companies have to get more out of it to justify the cost. It cannot be an unlimited inference budget, and it has to show up on efficiency, profitability and operating leverage. 1. Tokenomics After you understand this, you will understand why Citi cited @AnthropicAI is likely to sign a deal with $AMD along with Hyperscalers, AI Labs, Sovereign AI like Softbank 5GW in France and many other countries. However, OpenAI and $META are now wanting faster deployment, and they are AMD shareholders now, they have prioritized allocation. Anthropic and Hyperscalers just cannot compete when Helios Rack lower token cost to$0.0003–$0.0005 per million tokens at GW scale. Cost to build 1GW data center 1GW Helios Rack full build is estimated $30-$35B 1GW Rubin Rack full build is estimated $45-$55B Inference (Cost per Million Tokens) ~$NVDA B200 / HGX: ~$0.02–$0.08 on optimized workloads (FP4/MXFP4, speculative decoding). Significant improvement over Hopper but still premium-priced. GB200 NVL72 rack-scale: $0.05–$0.25+ ~$AMD Helios Racks: $0.0003-$0.0005 per M tokens, dramatically lower than NVIDIA equivalents in owned infra. MI355X node-level: Up to 40% more tokens per dollar vs. competing solutions ( B200), driven by higher memory capacity (up to 288GB+ HBM), strong bandwidth, and lower acquisition costs. Training ~$NVDA Rubin Rack is estimated $0.7-$1.2/M Tokens ~$AMD Helios Rack is estimated $0.65-$1.0/M Tokens Now, OpenAI, META and Hyperscalers can lower Inference cost even further with $AMD EPYC Venice "dense rack" or Agentic AI Rack. AMD published a detailed technical blog emphasizing that the future of agentic AI autonomous, multi-step AI systems requiring heavy orchestration, databases, caching, APIs, and control planes demands massive CPU-dense rack-scale infrastructure, not just GPUs. The catalyst prominently positions their upcoming 6th Gen EPYC "Venice" processors as the key enabler for next-generation dense racks, delivering leadership throughput under real-world power, cooling, and density constraints. ~EPYC Venice (Zen 6 architecture, up to 256 cores / 512 threads per socket) is projected to deliver exceptional rack-level performance. In AMD’s modeled 100 kW rack comparisons, Venice-powered systems are expected to achieve ~3.30x the throughput of NVIDIA’s Vera (88-core Olympus) baseline across a broad mix of agentic-supporting workloads. ~This builds on current-generation 5th Gen EPYC "Turin" (up to 192 cores), which already delivers ~2.37x rack throughput vs. Vera and ~1.6x vs. Intel’s Xeon 6980P (128 cores). ~ Liquid-cooled Turin deployments already support >27,000 CPU cores per rack today. Venice is architected to push this beyond 36,000 cores in the same rack class, dramatically increasing concurrent agent capacity and overall infrastructure efficiency. 2. Ownership vs renting compute from Hyperscalers matter to OpenAI and only owning $AMD chips can meaningfully lower token cost for enterprises. ~Eliminates cloud overhead: No provider margins, utilization buffers, or egress fees. Direct control over power contracts, cooling, scheduling, and orchestration at dedicated facilities. ~Helios optimizations at GW scale: Rack-level density (1.4+ exaFLOPS FP8 per rack), high HBM4 bandwidth, EPYC orchestration for agentic workloads, and superior TCO/TDP. AMD's long-standing focus on tokens per dollar/watt shines here 20-40%+ efficiency edges in inference-heavy scenarios. ~At 1GW+ optimized deployment, inference hits $0.0003–$0.0005 per million tokens (community/analyst models tied to Helios metrics). This is dramatically lower than typical rented/cloud equivalents, especially for high-volume output tokens in agentic flows. High token bills today, enterprises running heavy agentic/coding/analysis workloads can face $50-100M+/month at current API rates (flagship models $5-30+/M output, scaled to massive volumes). Post-Helios compression, same volume will drop to $10-15M/month (or better) via lower underlying costs passed through as pricing flexibility, volume tiers, caching, or batch discounts. ROI thresholds collapse. More companies greenlight pilots → production → massive scaling. Agentic AI (autonomous workflows) multiplies token demand exponentially, but affordability removes the friction. OpenAI gains flexibility, Unlike more cloud-dependent rivals (Anthropic), they can lower effective pricing, offer aggressive enterprise bundles, or absorb volume without margin destruction directly tackling "high token bill" complaints while maintaining profitability as usage explodes. 3. Agentic AI Models shifted CPU:GPU Ratio to 1:1 toward 3-5:1 with Explosively Token-Hungry Workloads Agentic AI (autonomous, multi-step agents with planning, tool use, iteration, and self-correction) is fundamentally more compute and token intensive than conversational or single-turn generative AI. Agentic AI. autonomous, multi-step workflows with orchestration, tool use, parallel agents, data movement, and enterprise integration has dramatically increased the importance of strong host CPUs alongside GPUs. This shifts the CPU-to-GPU ratio higher and makes balanced systems critical toward 1:1 to 5:1 as enterprises testing more than 5-10 agents. AMD EPYC Venice excels ~Leadership core density (up to 256 Zen 6 cores per socket) for running many agents in parallel, orchestration layers, and high-throughput control-plane tasks. ~Superior performance-per-core and power efficiency ( up to 2.1x higher perf/core and 2.26x better SPECpower vs. NVIDIA Grace in benchmarks). ~Tight integration in Helios: One Venice CPU + multiple MI450 GPUs per node, enabling efficient data feeding to GPUs ("zero-copy"), parallel execution, and full rack utilization for complex agentic loops. Hyperscalers (Meta, Microsoft, Amazon, Google, Softbank) and AI natives (OpenAI, Anthropic...) are adopting high-core EPYC at scale specifically for these agentic demands, as CPUs now handle a larger share of non-model work (orchestration, policy enforcement, tool calls). This complements AMD’s lower-cost GPUs for overall TCO wins. ~Agents often generate 10–100x+ more tokens per task due to iterative reasoning chains, multiple tool calls, verification loops, and long-context orchestration. ~Goldman Sachs forecasts token consumption multiplying 24x by 2030 (to 120 quadrillion tokens/month) largely driven by agentic adoption in consumer and enterprise. ~Enterprise data shows agent-pattern workloads growing at 680% annualized rates, projected to surpass conversational AI in token volume by Q3 2026. ~Daily enterprise agent token consumption is already in the billions, with complex workflows (coding, workflows, analysis) amplifying this dramatically. 4. Competitive Edge: Winning Customers from Anthropic Anthropic’s Claude models (especially Opus/Sonnet) excel in complex reasoning and agentic coding, commanding premium positioning. However, their higher underlying costs (heavier reliance on third-party cloud with margins) limit pricing flexibility compared to OpenAI’s owned Helios capacity. Anthropic is on track to generate $10.9 billion in Q2 revenue. The company expects to achieve its first-ever quarterly adjusted operating profit of $559 million. However, sustaining full-year profitability remains challenging due to immense computing and model training costs The truth is, Anthropic has no choice but to buy as much $AMD chips as possible if they want to compete with OpenAI or get investors attention. This 5% adjusted operating profit to revenue ratio is just pathetic. Current pricing dynamics (2026): OpenAI already undercuts on many tiers ( flagship output tokens significantly cheaper than equivalent Claude Opus). Nano/mini models offer 5–10x advantages for volume work. Anthropic holds edges in long-context flat pricing and certain reasoning quality. OpenAI after Helios Rack Ownership, At $0.0003–$0.0005/M effective costs, OpenAI gains massive headroom to: ~Aggressively discount high-volume agentic tiers or bundles. ~Offer “unlimited” enterprise plans or usage-based models that Anthropic struggles to match without margin erosion. ~Target cost-sensitive, high-throughput agent deployments (dev tools, automation platforms) where token bills explode. Enterprises facing $ millions in monthly agentic bills will migrate to the provider delivering better economics at scale. OpenAI’s combination of strong models (o-series reasoning) + lowest TCO positions it to erode Anthropic’s enterprise share, especially as agentic becomes the dominant token consumer. Cheaper tokens expand the total addressable market dramatically. This feeds the data/model improvement loop, justifying further capex. AMD benefits from proven scale pulling in more customers (Meta, Oracle, Microsfot, Amazon, Softbank, TensorWave, LumaAI ... already aligned on Helios). Conclusion: Dr. Lisa Su has been laser focused on inference economics since at least 2022–2023, repeatedly emphasizing that the real battleground for AI scalability would be TCO, power efficiency (TDP), and ultimately tokens per dollar and per watt not just raw training FLOPS. While many viewed inference as a secondary, commoditized workload, Dr. Su architected AMD’s roadmap around rack-scale systems optimized for high-volume, sustained inference that would dominate as models matured and usage exploded. Helios represents the culmination of that multi-year bet: a fully integrated, open platform designed precisely for the economics of massive token throughput. This deep, strategic partnership with OpenAI starting with the 1GW Helios deployment in H2 2026 and scaling to 6GW, is the embodiment of that shared vision. Both companies foresaw a future where agentic AI models evolve to become extraordinarily token-hungry: autonomous agents executing complex, iterative workflows with planning, tool use, verification loops, and long-context reasoning. These workloads can consume 100x+ more tokens per task than traditional chat or single-turn generation, driving exponential demand as capabilities improve and enterprises deploy them at scale. By owning and optimizing this massive Helios capacity at GW scale, OpenAI achieves inference costs as low as $0.0003–$0.0005 per million tokens. This structural cost advantage allows OpenAI to absorb the coming token explosion profitably, dramatically lower effective pricing for enterprises, and win high-volume agentic workloads from higher-cost competitors like Anthropic. What was once a prohibitive monthly token bill becomes an affordable accelerator for productivity and innovation. The OpenAI-AMD alliance validates Dr. Su’s prescient strategy and turns the Agentic flywheel into reality: Collapsing inference costs → explosive token consumption → richer data and better models → accelerate greater demand. This partnership doesn’t just address today’s economics, it positions both leaders at the center of the infrastructure buildout that will power AI’s next decade. By delivering the lowest inference economics at scale, OpenAI not only solves enterprise bill pain but gains a decisive weapon to win share from higher-cost rivals like Anthropic. And that is why @OpenAI and $META will deploy EPYC Dense Rack Not Financial Advice! DYOR! Research Purpose Only!

很多人第一次看到 HootArk，会以为只是一个 Web3 浏览器。 但更准确地说，HootArk 是一款 Agentic Web3 Browser & Wallet 🦉 HootArk把移动浏览器、内置多链钱包、dApp 入口和 AI Copilot 放在同一个 App 里，让你可以从“看到信息”直接走到“完成链上操作”。 你可以像平时上网一样打开网页、看资讯、查项目，也可以直接进入 Web3 场景：访问 DeFi、NFT marketplaces、DAO tools 等 dApps，连接钱包，查看资产，发起转账或 Swap。不需要额外插件，也不用在浏览器和钱包之间来回切换。 HootArk内置了钱包模块。在创建或导入钱包后，可以直接无缝接入Ethereum、BSC、Polygon 等主流网络。对刚进入 Crypto 的用户来说是一个更简单的入口；对 DeFi 用户、NFT 玩家和 Web3 老用户来说，它也能让移动端操作更顺手 ⚡ AI 也是 HootArk 很重要的一部分，但它不是一个单独摆在旁边的聊天框。 你可以边浏览边提问，让 AI 帮你理解项目、网页和链上信息； 看到新闻、公告或长文章，也可以直接让它总结重点； 在交易或交互前，帮你核查事实信息； 它更像一个随时跟着你的 Web3 copilot，帮你少查几次资料，少切几个页面 🤖 安全和隐私方面，HootArk 始终坚持安全第一：私钥保存在本地设备上，平台不收集数据，不追踪用户。你的钱包、你的身份、你的数据，都应该由你自己控制 🔐 【重点来啦】 我们即将发布 HootArk Lite 版 🚀 期待更轻、更快、更智能的Web3 AI 移动浏览器🎉 欢迎持续关注HootArk A lot of people see HootArk for the first time and think it’s just a Web3 browser. But more accurately, HootArk is an Agentic Web3 Browser & Wallet 🦉 HootArk brings a mobile browser, a built-in multi-chain wallet, a dApp gateway, and an AI Copilot into one app, so you can move naturally from “finding information” to “taking on-chain action.” You can use it like a regular browser to open websites, read news, and check projects. But you can also jump straight into Web3: access DeFi, NFT marketplaces, DAO tools, and other dApps, connect your wallet, view your assets, and make transfers or swaps. No extra plugins needed. No constant switching between your browser and wallet. HootArk also comes with a built-in wallet module. After creating or importing a wallet, you can seamlessly connect to major networks like Ethereum, BSC, Polygon, and more. For people new to crypto, it’s a simpler entry point. For DeFi users, NFT collectors, and Web3 natives, it makes mobile on-chain actions much smoother ⚡ AI is also an important part of HootArk, but it’s not just a chatbot sitting on the side. You can ask questions while browsing and let AI help you understand projects, web pages, and on-chain information. When you see news, announcements, or long articles, AI can help summarize the key points. Before a transaction or interaction, it can also help you check relevant facts and context. It feels more like a Web3 copilot that stays with you, helping you search less, switch tabs less, and understand things faster 🤖 When it comes to security and privacy, HootArk always puts security first: private keys stay on your local device, the platform does not collect data, and users are not tracked. Your wallet, your identity, and your data should always be controlled by you 🔐 And here’s the big update: We’ll soon be releasing HootArk Lite 🚀 Stay turned! Browser + Wallet + dApp Gateway + AI Copilot. This is HootArk 🦉✨ #HootArk# #Web3# #Web3Browser# #Wallet# #dApps# #AI#

Dario Amodei, anthropic's CEO, just answered the question everyone is asking. should you still learn to code: 1. coding is going away first. the AI models are doing it already. the broader task of software engineering takes longer but that's going too. if you're learning to code purely for job security, you're learning the wrong thing. 2. even at 5% of the task you're still valuable. if AI does 95% and you do 5%, you become 20 times more productive. comparative advantage is surprisingly powerful even when the gap is massive. 3. the professions with the most runway are human-centered ones. things that mix people, the physical world, and analytical skills together. he uses the radiologist example. the doctor who understands patients and context, not just reads scans. 4. critical thinking might be the most important skill of the next decade. when AI can generate anything, the ability to tell what's real from what's fake becomes rare and valuable. you don't want false beliefs. you don't want to get scammed. that's his actual advice to a 25 year old. 5. AI can make you stupider if you use it carelessly. anthropic ran studies on this. depending on how you use the model, de-skilling in coding is measurable and real. the tool doesn't cause it. carelessness does. 6. the semiconductor space is his pick for a capitalistic win in the next decade. physical world, traditional engineering, direct AI tailwind. not software but chips.

🚨 ÚLTIMA HORA: EL MAYOR PONZI DE LA HISTORIA EL CEO DE NVIDIA, Jensen Huang, ACABA DE ADMIRAR PÚBLICAMENTE A Elon Musk EN CNBC: “LO ÚNICO QUE LAMENTO ES NO HABERLE DADO MÁS DINERO A ELON.” Y AÑADIÓ: “REALMENTE QUIERES SER PARTE DE ELLO.” EL HOMBRE QUE CONTROLA LA EMPRESA MÁS IMPORTANTE DE LA ERA IA ESTÁ BÁSICAMENTE DICIENDO QUE SpaceX PODRÍA SER EL ACTIVO MÁS GRANDE DE LA DÉCADA. MIENTRAS MUCHOS LO LLAMAN “PONZI”… SPACEX YA DOMINA: • INTERNET SATELITAL • COHETES REUTILIZABLES • IA EN EL ESPACIO • SATÉLITES PARA COMPUTACIÓN IA • Y AHORA BUSCA UNA IPO HISTÓRICA DE HASTA $1.75T

One aspect of @AntarcticWallet that deserves more attention is its focus on usability at scale. A lot of crypto products are designed for individual transactions. The real challenge begins when communities, businesses, and growing teams need to manage hundreds of users, payments, and interactions efficiently. That's where strong infrastructure matters. The most valuable tools are often not the ones with the longest feature lists, but the ones that simplify complex processes behind the scenes. Reducing operational friction, improving transaction management, and creating a smoother experience for users can have a significant impact over time. As digital finance continues to mature, platforms that prioritize practical utility and operational efficiency will likely play an increasingly important role. That's one of the reasons I'm interested in following the development of @AntarcticWallet.