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余承东这么说，只会让行内人看不起...如果他说的是真的，那从2021年全球“遥遥领先”到2022年底OpenAI横空出世，菊厂拍马都追不上，这落后的速度也太快了

今田学 1st EP 「a day in my life」 2022年11月にリリースした5曲入りEPです。 各サブスクリプションサービス、youtubeなどでお聴きいただけます！

戴威大家还记得吗？北大高材生，ofo小黄车创始人！ 1991年出生的戴威，人生前26年顺风顺水。父亲是国企高管，他18岁考上北大。2014年去青海支教时，因为骑车太远，萌生了共享单车的想法。 回北大后，他和同学凑了100万，在校园里搞起了共享自行车，一开始只有200辆车，用户交1块钱就能骑。2015年，资本看中这个项目，聊了半小时就投了1000万。 之后ofo像坐火箭一样：10个月融了近90亿，半个互联网圈都是股东。巅峰时全球100多座城市，投放超1000万辆小黄车，日订单3200万单。26岁时，戴威身家35亿，上了胡润百富榜，是第一个白手起家的90后。 他花钱很大手大脚，1000万请明星代言，2000万给卫星冠名，广告铺满北上广深。 2017年一年，光推广就烧了好几亿。 但共享单车就是个烧钱生意。车坏得快、补贴猛、运维贵，每天亏几千万。融资跟不上烧钱，戴威就把用户押金当成了自己的钱。当时全国1亿多人交了99或199元押金，总额几百亿，他拿去造车、打广告。 2018年，用户突然发现押金退不出来了，1500万人排队要钱，公司楼下天天挤满讨债的人。 面对危机，戴威做了三个致命决定：不合并、不让查账、不接受收购。结果资本跑光，资金链断裂。他发内部信说：会还每一分钱，没多久就消失了。 后来大家发现他跑去了美国。先在西雅图搞移动电源，没搞成，2022年在纽约开连锁咖啡店，用老股东1000万美元投资，估值一度4000万。但2023年底，5家店关了4家，基本停滞。 国内这边，ofo公司早就没钱没资产，法院执行不了。戴威成了老赖，被限制消费40多次。2026年5月，ofo APP、小程序彻底瘫痪，所有退款通道关闭。那15亿押金，彻底没了。 1500万普通人扫码骑最后一公里时，出于信任交的钱，就这么打了水漂。而戴威还在美国继续他的创业。 很多人已经忘了自己那99块，但每次看到他的消息，心里总不是滋味。​毕竟，那不是什么大风刮来的钱。是无数普通人扫码骑过那最后一公里时，出于信任交的钱。 对此，你们怎么看？

$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!

📅 世界杯开幕预测 · 2026年最精彩的一届 2026世界杯今晚凌晨正式打响 48支球队，16个城市，美国+加拿大+墨西哥三国联办，史上规模最大的一届 我其实是不看足球的，是昨晚 @babemiki209 告诉我，她原本想回加拿大现场看球赛，因为这届世界杯真正让人无法平静的原因只有一个 这是最后一次 👑 巨星谢幕倒计时 🇦🇷 梅西 · 39岁 · 2022卡塔尔终于圆梦，这是他的告别演出 🇧🇷 内马尔 · 34岁 · 伤病缠身仍归来，巴西人的最后期待 🇵🇹 C罗 · 41岁 · 超越时间的男人，这次真的是最后一次 三个人同台，可能是足球史上最后一次 不看进球，就看这件事本身，已经值回票价 我哪懂啊，我只懂看帅哥 🔮 预测市场今年最火 随便打开预测市场平台的页面 冠军归属、小组出线、梅西进球数、内马尔首场是否上场……每一个问题背后都是真金白银在博弈 预测市场有意思就在这里，它不是在猜，是在用钱表达判断。赔率会实时反映大家对这届世界杯的集体定价。比FIFA的官方预测更诚实 📊 小组赛首战：🇲🇽墨西哥 VS 🇿🇦南非 👉 今晚开幕赛，你的判断是？ □ 🇲🇽 墨西哥 □ 🇿🇦 南非 □ 平局 ⭕️ 战力速览 🇲🇽 墨西哥 ▪️ 东道主 + 高海拔主场，对手体能消耗大 ▪️ 近8场不败，6胜2平，最近5-1大胜塞尔维亚 ▪️ 阿兹台克球场底蕴加持，大赛经验无出其右 🇿🇦 南非 ▪️ 非洲代表，速度快、冲击力强 ▪️ 2010年东道主，有世界杯DNA ▪️ 但客场高原作战，体能是最大变数 我的判断：墨西哥赢，但别指望大比分。（虽然我也不太懂），听朋友分析的，主场+海拔双重优势，南非体能扛不住下半场 有没有人今年跟着世界杯做预测市场的？ 来评论区说说你押哪队冠军 👇 DYOR 非投资建议 #世界杯2026# #WorldCup2026#

Amard Pagan 6’0 155 Guard Penn State Lehigh Valley USCAA D2 2022-23 Stats (20 Games): 📈 10.0 PPG 2.6 RPG 43% FG 37% 3PT 73.6% FT 2024-25 Stats ( 20 Games): 📈 7.3 PPG 2.6 RPG 40% FG 2022-2023 | FRESHMAN Appeared in 20 Games, Starting 6 Games Averaged 10.0 PPG Scored a season- high of 17 points three times against Harrisburg Community College (11/15/2022) Thaddeus Stevens Tech (1/17/2023) Northampton Community College (1/19/2023)

En junio de 2022, las empresas cotizadas tenían 130.000 bitcoin:native en balance Hoy: 1.251.951 bitcoin:native Ranking completo de 52 tesoreras, en tiempo real → +865% en 48 meses. Casi el 6% de todo el bitcoin que existirá, ya está en tesorerías corporativas. Y la mayoría aún no sabe que esto está pasando