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$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!

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Elon Musk identified which jobs go first, and it destroys every assumption about who’s safe. Musk: “AI is going to take over those jobs like lightning. Anything that is digital, which is like just someone at a computer doing something.” Not factory workers. Office workers. The people who spent decades assuming education and desk jobs meant security are actually first. Musk: “Anything that’s physically moving atoms… those jobs will exist for a much longer time.” Output is a file? Vulnerable. Output is physical? Protected. That’s the entire framework. Musk: “AI is really still digital.” AI doesn’t need a body. Doesn’t need an office. Just needs access to the same software you use. Executes faster. Never tires. Costs nothing to scale. But it can’t weld. Can’t wire a building. Can’t fix pipes or work soil. Musk: “Literally welding, electrical work, plumbing. Those jobs will exist for a much longer time.” Trades aren’t the vulnerable jobs. They’re the durable ones. Physical presence, real-world adaptation, manual dexterity provide protection no digital credential offers. Analyst, accountant, paralegal, programmer, anyone producing files and documents, automates first because digital work is exactly what AI does natively. Person moving atoms has natural defense. Physics, unpredictable environments, material resistance create friction AI can’t scale past. Person moving bits has nothing. No friction. No physical barrier. Just software AI already operates better than most humans. The assumption that desk work and degrees represent safety just inverted completely. College graduate producing documents faces faster displacement than the electrician producing installations. Society spent generations telling people trades were beneath them. Pushed everyone toward offices and screens. Turns out the people who didn’t listen built the most automation-resistant careers. Most ironic outcome of the AI revolution. The work society treated as inferior turned out to be the work society couldn’t replace. And the work society valued most turned out to be the easiest to eliminate.

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AI Practical Use #3#: Let AI help you with Excel data analysis. AI 实用玩法第 3 个： 让 AI 帮你做 Excel 数据分析。 Here is a very common office situation: You have an Excel file with sales data, costs, profit, regions, products, and dates. Normally, you may spend 2 hours writing formulas, checking data, making summaries, and building charts. But with AI, you can finish the first draft in about 10 minutes. 一个很常见的办公场景： 你手里有一份 Excel 数据， 里面有销售额、成本、利润、区域、产品、日期。 以前你可能要花 2 小时： 写公式、查数据、做汇总、看趋势、做图表。 现在可以先交给 AI， 10 分钟生成初步分析结果。 You don’t need to manually type every complex formula. Let AI help you: Build formulas Summarize key findings Find abnormal data Compare trends Suggest chart formats Create a report structure 你不需要自己一个个输入复杂函数。 可以让 AI 帮你： 生成公式 总结关键结论 找出异常数据 对比趋势变化 建议图表形式 生成汇报框架 Here is a simple prompt: 这里有一个简单提示词： Please analyze this Excel data. Help me build the right formulas, summarize the key findings, find possible errors or abnormal values, and suggest the best chart or report format. I will review and verify the final results. 中文版本： 请分析这份 Excel 数据。 帮我生成合适的公式，总结关键结论，找出可能的错误或异常值，并建议最适合的图表或汇报格式。 最终结果由我来审核确认。 The key idea is simple: AI does the heavy first draft. You review the logic and final result. 核心思路很简单： AI 负责先把复杂工作做出来， 你负责审核逻辑和最终结果。 Before: 2 hours manually writing formulas. After: 10 minutes with AI assistance. 以前： 手动写公式、做分析，可能要 2 小时。 现在： 借助 AI，10 分钟先完成初稿。 AI is not here to replace your judgment. It helps you save time on repetitive work, so you can focus on checking, thinking, and making better decisions. AI 不是替代你的判断力。 它是帮你节省重复劳动的时间， 让你把精力放在审核、思考和决策上。 Let AI write the formulas. You review the results. 让 AI 写公式， 你负责审核结果。 That is a smarter way to work. 这才是更聪明的办公方式。 #ChatGPT# #AI# #AITools# #Excel# #ExcelTips# #DataAnalysis# #Productivity# #WorkSmarter# #OfficeWork# #BusinessTools# #Automation# #DigitalTools# #TechTips# #FutureOfWork# #PromptEngineering#

After more than a decade of development, Wube Software has announced that Factorio’s upcoming 2.1 update will be the game’s last major gameplay update. In a blog post, the studio explained that Factorio has “reached a good place to conclude active gameplay development” and that “the game feels complete.” After years of updates, improvements, and the recent Space Age expansion, the developers believe they have achieved what they set out to create. Factorio first entered early access in 2016 and became one of the most acclaimed factory-building and automation games ever made. Wube said it will continue supporting the game with bug fixes and maintenance updates. The studio is now preparing to focus on future projects.

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怎么低成本搭建自己的AI助手？ deepseek R1模型发布以来，各种AI应有又有了一波大的爆发，但是数据保密成了一个不容忽视的问题。如果想较低成本地搭建一个私人应用的AI助手，可以自己调试生成限制，调试输出文本内容，数据不外传，成本尽可能低，不知道是不是可行。 本文详细介绍如何通过Dify和Chrome MCP的结合，在3分钟内搭建一个能操作网页的AI助手。文章提供了从环境准备、Dify部署、Chrome MCP安装到配置的完整步骤，展示了AI助手在自动网页搜索、表单填写、数据抓取等场景的应用，并解释了工作原理、问题排查及进阶技巧，让读者能够零代码实现网页自动化任务。 你是否想过让AI不仅能回答问题，还能直接操作浏览器帮你做事？比如自动填写网页表单、抓取特定数据、或者点击按钮？现在，通过Dify和Chrome MCP的结合，你可以在3分钟内搭建一个能真正"动手"操作网页的AI助手。 本文将手把手教你如何在本地部署Dify并配置Chrome MCP服务器，打造你的私人网页自动化助手。 准备工作：确保你的环境就绪 在开始前，请确保你的系统已安装： •Docker 和 Docker Compose（用于快速部署Dify） •**Node.js 18+**（用于运行Chrome MCP服务器） •Git（用于克隆项目仓库） 第一步：快速部署Dify（仅需1分钟） Dify是一个强大的LLM应用开发平台，我们通过Docker快速部署： # 创建项目目录 mkdir dify-chrome-mcp && cd dify-chrome-mcp # 下载Docker部署配置 curl -o docker-compose.yml # 启动Dify服务 docker-compose up -d 等待1分钟左右，访问 http://localhost:80 就能看到Dify管理界面。首次使用需要创建账号并完成初始化设置。 第二步：安装Chrome MCP服务器（1分钟） 打开新的终端窗口，安装并启动Chrome MCP服务器： # 安装Chrome MCP服务器 npm install -g @modelcontextprotocol/server-chrome # 启动Chrome MCP服务（会自动打开Chrome浏览器） server-chrome 服务启动后，你会看到类似下面的输出，表示MCP服务器正在9999端口监听： Server running on http://localhost:9999 Chrome browser launched and connected successfully 第三步：在Dify中配置Chrome MCP（1分钟） 现在回到Dify管理界面，进行关键配置： 1.进入设置 > 模型供应商 > MCP服务器 2. 点击添加MCP服务器 3. 填写配置信息： •服务器名称: Chrome-Automation •服务器URL: http://localhost:9999 4. 点击验证并保存，看到绿色成功提示 效果验证：让AI帮你操作网页 配置完成后，你就可以在Dify的Playground中测试你的网页自动化AI助手了。 试试这些实用场景： 场景1：自动网页搜索 请打开百度首页，在搜索框中输入"最新AI技术发展"，点击搜索按钮，然后告诉我第一页的搜索结果标题。 场景2：自动填写表单 请打开"张三"，邮箱栏填写"zhangsan@email.com"，在留言区填写"咨询产品信息"，然后点击提交按钮。 场景3：数据抓取与分析 请打开 工作原理：为什么这很厉害？ 这个组合的厉害之处在于分工明确： •Dify：负责与LLM对话，理解你的自然语言指令，并将其分解成具体的浏览器操作步骤 •Chrome MCP服务器：负责实际控制Chrome浏览器，执行具体的网页操作命令 •LLM（大语言模型）：作为大脑，理解你的意图并规划操作流程 常见问题排查 server-chrome 2.连接失败：检查Dify和Chrome MCP服务器是否在同一个网络环境下，防火墙是否允许9999端口通信 3.操作超时：复杂网页加载需要时间，可以增加超时设置：server-chrome --timeout=60000 4.权限问题：在MacOS/Linux上可能需要权限：sudo npm install -g @modelcontextprotocol/server-chrome 进阶使用技巧 掌握了基础用法后，你还可以尝试这些高级功能： •多页面管理：同时控制多个浏览器标签页，完成更复杂的任务 •用户身份保存：让AI记住登录状态，下次直接操作无需重新登录 •定时任务：结合Dify的工作流功能，设置定时自动化任务 •异常处理：教会AI识别操作失败的情况并自动尝试替代方案 如何学习大模型 AI ？ 由于新岗位的生产效率，要优于被取代岗位的生产效率，所以实际上整个社会的生产效率是提升的。 但是具体到个人，只能说是： 最先掌握AI的人，将会比较晚掌握AI的人有竞争优势。 这句话，放在计算机、互联网、移动互联网的开局时期，都是一样的道理。 #AI# #AIAgent# @grok @xai

One thing users don’t always realize about Panora: We’re not just building products for today’s DeFi users. Our 2026 focus is to make Panora the execution layer for on-chain trading on @Aptos - powering better routing, automation, APIs, advanced trading, and AI-driven workflows. Watch the Panora x Aptos clip 👇

装上这个插件后，Claude Code 体验直接起飞。 Anthropicn quietly 发布了官方插件 claude-code-setup，能把 Claude Code 从'还不错'升级成真正的 AI 开发环境。 它会扫描你的项目并推荐： → hooks → skills → MCP servers → subagents → automations 然后一步步帮你配置好。 大多数人还在用原生版本… 怪不得体验这么混乱。 真正的威力在周边生态。 立即安装： /plugin install claude-code-setup@claude-plugins-official 赶紧收藏