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オレンジ田中さんのYouTube、てつぶらにゲストとして呼んでいただきました！！ クリスマス緊急特番！元SKE48矢神久美ちゃんがゲスト出演してくれました！てつぶら#36〜四日市編〜️# @YouTubeより

On this day in 2002... the Los Angeles Lakers became just the 5th team in NBA history to win 3 consecutive NBA titles! ▪ Shaq: 36.3 PPG, 12.3 RPG and 2.8 BPG for the series, winning his 3rd straight NBA Finals MVP🤯 ▪ Kobe: 26.8 PPG, 54.5 3P%, third-youngest player to win 3 rings💍 ▪ Phil Jackson: 9th title, completing the 3-peat for the 3rd time in his career 🏆

#投资周记# 第222周 名称/占比/盈亏 中证A500/83.58%/36.72% 恒生科技/7.34%/-5.59% 当前账户盈亏率：+102.43% 这周又是大跌，不过还好周五又拉起来点，看起来四周前加仓的位置还是不太对。😮‍💨 想了一下，暂时没必要动，算了，继续观察。👀

世界杯开赛，各大交易所为了抢流量砸了几千万美刀。边看球边撸交易所活动 几家大所的规则 精简版羊毛攻略，各取所需： 1. 0撸低保流：OKX（16.66 BTC）绝对的无本生利。签到、做任务攒 XP 积分就能去猜 190 多个盘口。零门槛白嫖真 BTC，不管看没看过球，这个必须顺手点了 2. 佛系大肉池： Wallet 关联的标的，200 万刀死池子按小组赛、决赛阶段硬发。最多选 5 支队押全赛程，适合拿了筹码直接躺平的波段玩家（除了 $2M 固定奖池外， 有独立的 Predict Points（PP）积分系统，并且已与 Binance Wallet 深度集成 一鱼双吃） 2. 合约/战神流：MEXC（1.36M USDT）& Bybit（$1.0M）MEXC：玩 YES/NO 合约，支持最高 200x 串关，连胜直接开高倍数每日 Diamond 奖池（$20k） Bybit：交易刷积分注入动态池，参与度越高池子越大。高频率交易员专属 3. 链上对冲流：Bitget（$200k）& Gate（$500k+）Bitget 深度绑了 Polymarket，钱包内直通链上。有 20 天连胜奖励和首单亏损保护（$50k 池子），适合拿去和别家盘口做双向对冲，锁死利润。 最后说个安全问题： 这几天各种假链接、钓鱼推文满天飞。老手都知道别乱点评论区和私信的任何链接。直接回 App 首页找 Banner 进，或者认准官方推特。 别羊毛没薅到，把本金给整没了。球赛要看，钱包护好。 今年你们看好哪支队？

在日本的倒计时5个小时，发现日本地铁站都贼大，喝完咖啡在地铁站里找厕所，咖啡店里竟然没有厕所，在加拿大的咖啡店不设厕所是不可思议的。市政府是不会批这种堂食生意的，还必须男女厕所，厕所门3英尺，36寸宽，门往里推的话，后面还要有个无障碍60英寸的无障碍的轮椅旋转直径。 另外我为了找地铁厕所走了近150米，我寻思，要是顾客尿急怎么办？要是有男性前列腺尿频怎么办？我是不是多虑了，看来日本人一本肾和前列腺都挺好。 日本的地铁公厕比我想象中干净，厕纸满满，毫无异味。 另外还有个发现，日本的马桶🚽贼短，坐厕的话，丁丁会碰壁，这是我不爽的地方。

New @ThePeelPod with @samdblond This is his first podcast since starting @MonacoGTM, and our 2-hour conversation walks through everything sales, marketing, and GTM in a AI-native world. Thanks to @numeral, @FlexSuperApp, @Amplitude_HQ, and @merge_api for supporting this episode! Watch here + links below. Timestamps: 0:00 Scaling Brex to $12B 1:14 How AI speeds up prospecting and TAM building 5:19 Using AI to get more leverage 9:15 Incubating Monaco at Founders Fund 12:56 Innovator’s dilemma in AI 15:57 AI companies should build full platforms, not wedge products 23:30 Revenue is just a math equation 27:18 Two ways AI increases conversion rates 36:56 AI will never replace spending time with customers 39:46 Don’t measure the impact of brand marketing 49:03 Your marketing must be different (and hard) 58:39 Customer discovery calls and working with design partners 1:03:03 The zero to 100 launch 1:11:00 Monaco’s launch playbook 1:19:00 Send gifts that are unique and social 1:22:17 Naming your company 1:28:04 Founders should send early outbound 1:32:38 How multi-channel augments AI outbound 1:39:42 Using intent signals and outreach timing to increase conversions 1:43:28 Two common ways founders mess up when scaling revenue 1:50:22 Monaco’s Forward Deployed AE's

$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!

兄弟们，醒醒！ 这才是真正的财富密码！ 纳指近十年最大回撤才-36.4% 标普也才-33.9% 每次都精准把三倍做多ETF干爆仓，然后直接开启下一轮超级牛市！ 卧槽，一般人永远在底部乱抄、乱梭，结果越抄越亏。 真正的高手呢？ 死等那一波大回撤！ 等它把TQQQ、FNGU这些三倍杠杆干到地板价， 直接把全部身家、全部现金杀进去！ 或者更狠一点：All in 三倍苹果 + 三倍英伟达！ 十年暗室，一灯即明。 你的命运，从来都在自己手上。别再手动了兄弟们，这波等到了就是人生开挂！ 敢不敢干？你怎么看呢？

刷微博看到有人称自己的 NAS 一个月耗电 70 度左右，这用得也太凶了。 反观懒猫微服03，我们自己测了一下，满功耗运行一年的电费也才219.36。 更别说绝大多数时间做不到满功耗运行🤪 买懒猫微服，得到的是全方位的服务哈哈哈，包括性能与省电的均衡统一。