与「Excel」相关的搜索结果

在日本做了几年独立 AI 工程师,聊几个只有真正在这里干过才知道的事。不是签证、收入、市场分析那些,是日常工作里那些不会出现在任何攻略帖里的细节。 第一个没人告诉你的事:在日本,中国人做 AI 有一个非常奇怪的信用加成。 日本企业对"AI"这件事的认知来源主要是两个:美国和中国。美国代表前沿(OpenAI、Anthropic、Google),中国代表速度(DeepSeek、千问、字节)。你是中国人,天然被归到"速度快、实战经验丰富、见过大场面"这个认知框里。 日本本土的 AI 工程师大多数是从学术界转过来的,理论功底扎实,但生产部署经验偏弱。你跟日本客户说"我在中国的互联网公司做过日均千万级 DAU 的数据系统",他们的反应不是"哦",是"えー、すごいですね(哇好厉害)"。因为日本本土很少有这种体量的实战场景。 这个加成不是永久的,干砸一个项目就没了。但它给了你一个很好的开局:第一次见面时,客户对你的预期天然比对一个日本本地工程师高半格。 第二个没人告诉你的事:日本客户最怕的不是你技术不行,是你"突然消失"。 在日本商业文化里,"飞ぶ"(消失/跑路)是对合作关系最大的恐惧。他们之前遇到过的外国 freelancer 里,有人项目做到一半签证到期走了,有人拿了预付款之后联系不上了,有人说好的交付日期突然说"还需要两周"然后反复延期。 所以日本客户考察你的第一优先级不是"你有多强",是"你靠不靠谱"。靠谱的定义极其具体:说好周五交就周五交,邮件当天回,电话接得到,出了问题第一时间主动说而不是藏着。 我刚来日本的时候不理解这一点,觉得"技术好就够了"。后来才明白,在日本,你交付质量 90 分但每次都准时,比你交付质量 98 分但偶尔迟到一次,在客户心里的评价要高得多。信赖感(信頼感)是日本商业关系的地基,地基不稳什么都白搭。 第三个没人告诉你的事:在日本做 AI 落地,最有效的销售话术不是"AI 能帮你省多少钱",是"不用 AI 你会被同行甩开多远"。 日本企业的决策动机跟中国企业不一样。中国企业决策靠 ROI:"花这些钱能赚回来多少?"算得过来就干。日本企业决策靠"危機感":"不做这件事会不会落后于同行?"同行都在做,我不做,不行。同行都没做,我为什么要第一个冒险? 所以你跟日本客户谈 AI,最有效的切入方式不是给他算 ROI,是告诉他:"你的竞争对手 XX 已经在用 AI 做这件事了。"这句话在日本商业文化里的杀伤力,比任何 ROI 计算表都大。 当然,前提是你说的是真的。日本圈子小,胡说被抓到一次,你在整个行业就废了。 第四个没人告诉你的事:中日英三语是一个被严重低估的壁垒。 表面上看,语言只是沟通工具。实际上,三语能力让我能做到一件几乎没有竞争者能做的事:用英文读 Anthropic 的 system card 和最新的技术文档,用中文跟国内的 AI 社区保持同步,用日语跟客户的业务方和技术方深度沟通。 全球最前沿的 AI 信息首先出现在英文世界,通常晚一到两天出现在中文世界,晚一到两周出现在日文世界。我能在信息出现的第一天就消化它,然后在一周内把它变成日本客户能理解和使用的方案。 这个时间差就是我的定价权。日本本地的 AI 工程师要等日文翻译或解读出来才能跟进,美国的 AI 工程师不会日语进不了日本市场。中间这个位置,人极少。 第五个没人告诉你的事:我交过最贵的学费是"把中国的工作习惯带到日本"。 刚来的时候我犯了几个现在想起来都想扇自己的错误: 给客户发了一个方案,里面直接写"你们现在的做法效率很低,应该换成 XX"。在中国这叫直接、高效。在日本这叫"失礼"。日本的方式是:"贵社目前的方式当然是经过深思熟虑的(先给面子),不过如果考虑未来的扩展性(给台阶),或许可以参考一下这种方法(才提建议)。"同样的意思,包装方式完全不同。我花了大概半年才把这个习惯改过来。 还有一次,客户说"検討します"(我们考虑一下),我以为是真的在考虑,等了两周去跟进。后来才知道这句话在很多场合的真实含义是"我们不打算做,但不好意思当面拒绝你"。在日本,"不"很少被直接说出口,你得学会听懂那些"不是不"的"不"。 还有一次把项目进度做成了飞书文档共享给客户。客户完全不知道飞书是什么,打不开。后来老老实实改用 Excel + 邮件。在日本企业里,Excel 和邮件是永远不会错的选择。你觉得落后,人家觉得稳当。 第六个:最意想不到的获客渠道。 我以为在日本获客要靠 LinkedIn 或者行业展会。实际上我最有效的获客渠道有两个:一个是 X(推特),另一个是日本特有的"勉強会"(学习会/技术分享会)。 日本的技术社区有一种独特的文化:定期办免费的技术分享会,大家轮流讲自己在做的东西。你去讲一次,讲得好,会后有人来跟你换名片(是的,日本还在用纸质名片,而且交换名片有一套完整的礼仪),两周后邮件来了:"之前听了您的分享,我们公司正好有一个类似的课题,方便聊一下吗?" 这种获客方式成本为零,但信任转化率极高,因为对方亲眼见过你讲东西,知道你是真的懂而不是嘴上说说。 最后说一句总结。 在日本做独立 AI 工程师,最核心的能力不是技术,是"翻译"。不是语言翻译,是把全球最前沿的 AI 技术,翻译成日本企业能理解、敢尝试、用了之后能看到结果的东西。技术只是原料,翻译才是手艺。 而这个"翻译"能力是没法被 AI 替代的,因为它的核心不是信息转换,是理解两种完全不同的商业文化各自在怕什么、想要什么、能接受什么。这种理解只能靠在两边都踩过坑才能长出来。 所以如果你问我在日本做 FDE 最大的壁垒是什么,不是技术,不是签证,不是日语,是你愿不愿意花几年时间在一个节奏完全不同的市场里,把那些只有踩过才懂的坑全部踩一遍。 踩完了,壁垒就是你自己。

$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!

Excellency. Congratulations on having served as Prime Minister for over 4,000 days. I hope you every success in your future endeavours. I look forward to meeting you in the near future. Yours sincerely, KISHIDA fumio Chairman, The Japan-India Association @narendramodi

Step aside. (Berlin) has one of the cleanest, most thoughtfully designed AI creative suites I've seen. You get access to a multitude of frontier models in one polished platform: Seedance 2, Veo 3.1, GPT Image 2, Nano Banana 2, Voice Cloning, Agent workflows, advanced editor + more. What stands out: Full EU (Germany) hosting, GDPR compliant, SOC 2 aligned, private content by default. The concept and layout are excellent. Only downside so far — very limited free tier (hard to even test one generation without signing up for paid). Structure precedes perspective. Worth the entry for EU creators?

RIFLEBIRDS OF PORTLAND REVIEWED IN THE LANGUAGE OF GOETHE “(‘April’) has aged remarkably well—or rather, it is a timeless work that sounds as though it had just been recorded.” Thank you to Thomas Waldherr for the excellent review of our reissued debut album “April” at German music site Folker World (folker - song, folk & world.) Link to the entire review below. “April” vinyl and CD available via Select-O-Hits in the US and Proper Music Group in the UK/EU.

According to @sleepagotchi Sleep Coach, I've just had a few nights of excellent sleep I'm still not entirely sure about the "perfect sleep" part, but at least Dino seems happy to keep getting new furniture for his crib And yes, it turns out my body can't function on coffee and Web3 energy alone Have anyone tried the Sleep Coach feature? How would you rate your own sleep patterns?

Github 最多好评的开源【美股智能分析系统】 我刚发现这个库好像我还没分享过，实在是罪过啊。作为 Github 最多星星的仓库--41.5K，含金量我就不多说了。 主要功能如下： AI 决策报告：核心结论、评分、趋势、买卖点位、风险警报、催化因素、操作检查清单。 多市场数据聚合：A股、港股、美股、ETF；行情、K 线、技术指标、资金流、筹码、新闻、公告和基本面。 Web / 桌面工作台：手动分析、任务进度、历史报告、完整 Markdown、回测、持仓、配置管理、浅色 / 深色主题。 Agent 策略问股：多轮追问，支持均线、缠论、波浪、趋势、热点、事件、成长、预期等 15 种内置策略，覆盖 Web/Bot/API。 智能导入与补全：图片、CSV/Excel、剪贴板导入；股票代码/名称/拼音/别名补全。 自动化与推送：GitHub Actions、Docker、本地定时任务、FastAPI 服务和企业微信/飞书/Telegram/Discord/Slack/邮件推送。 仓库地址： 这个库功能十分强大，推荐大家使用。 我是尼卡，平时会持续分享 AI、美股、Web3 相关有用又有趣的工具和项目，感兴趣的话欢迎关注，下次见～

让 AI 帮你做个 PPT 它给你生了一堆 div 和胡乱排版的 layout。点开一看，比你自己动手做的还丑。 更烦的是 Excel——公式乱写、格式全飞，生成完了你还得从头到尾手动改一遍。那你还不如自己做。 MiniMax 把这四个文档 skill 开源了。PPT、PDF、Excel、Word，每一个都是正经能用的水平。 PPT 有 18 套配色 4 种风格，不是瞎排。 Excel 生成完会自己跑一遍公式，检查 #REF#! #VALUE#! 这些低级错误。 PDF 15 种封面，字体颜色自动适配，直接打印没问题。 不用 API Key，装完就能让 AI 帮你写文档。

Jason Calacanis warns developers about Sam Altman and OpenAI: “If I were any kind of developer, I would never work with Sam Altman and OpenAI, This is a warning for anybody dumb enough to use Sam Altman’s OpenAI API, Sam is an incredibly savvy person, and he wants every bit of revenue from the ecosystem. He's gonna study how you're using the API, which he has the right to do. Sam Altman comes from the Zuckerberg school of business, which is: give people access to your tools, study them, and like the Borg, steal every innovation they create . Exactly like Bill Gates did at Microsoft; they let people build Lotus 1-2-3, and then they did Microsoft Excel. They let people build a product called WordPerfect and WordStar, and then they built Microsoft Word."