与「Transformers」相关的搜索结果

Meet China’s real-life Transformers. These powerful rescue platforms deployed to SW China’s Guangxi provide vital lifelines in raging floods and carry people out of danger.

Google Research在2024年悄悄开源了一个时间序列模型。 除了做预测的人，没人注意到。这是一个错误。 这个模型叫TimesFM。 论文发在ICML 2024，标题是"一个用于时间序列预测的解码器架构基础模型"。 核心思路直接借鉴语言模型：先在海量数据上预训练，然后用同一个模型预测任何新序列，不需要重新训练。 过去几十年，时间序列预测一直是一个数据集一套模型的模式。 你收集某个问题的数据，选一个模型架构。 在这个数据上训练，验证。如果问题变了，从头来过。 每个数据集都是一个独立项目。 每个场景都是一条独立流水线。 TimesFM改变了这件事，它在大量跨领域、跨频率的时间序列数据上预训练。 训练完成后，面对任何新的时间序列都能直接预测，零样本预测。 2025年9月，Google发布了2.5版本。 参数从500M降到200M，上下文从2048拉到16K。 加了一个30M的分位数预测头，能同时输出点预测和10%到90%的置信区间。 更小的模型。更长的上下文。 更好的结果。这很少见。 实际影响很具体，200M参数跑一张GPU就行。 16K上下文意味着你可以喂五年日数据，模型能抓住年度季节性。 分位数预测头意味着你不只有一个预测值，还有不确定性范围。 Google内部已经在用了。BigQuery ML里用SQL直接调。Google Sheets的Connected Sheets里内置了。Vertex AI提供了Docker端点。 开源版本免费，两行Python。 加载模型，调用forecast。输入numpy数组，输出预测结果。 2026年4月，Google加了通过HuggingFace Transformers和PEFT用LoRA微调的能力。 这意味着你可以用少量领域数据把预训练模型适配到你的具体场景。 时间序列预测不是一个光鲜的领域。没有病毒式传播的演示。没有十亿美元的消费产品。 但每个管理库存、预测需求、监控设备、交易金融工具的企业都依赖它。 TimesFM把这个行业最好的工具变成了pip install就能用的东西。 地址见评论区👇🏻

Jensen Huang just handed every AI cloud investor the clearest framework for picking winners and the question is who actually understands what he said (Save this). Compute is not just infrastructure anymore but rather revenue, and performance per watt is the mechanism by which that revenue becomes profit. The argument Jensen made at Computex deserves to be unpacked fully because it completely reframes the neocloud investment thesis. Every AI factory operates inside a fixed power envelope and once your data center is built and your power contracts are signed, that ceiling does not move. One gigawatt means one gigawatt and the only variable that determines how much money you make is how many profitable tokens you can squeeze out of each watt of electricity flowing through your facility. An operator who chooses cheaper, lower efficiency chips because the upfront cost looks attractive is not saving money and they are permanently handicapping their revenue ceiling for the life of that asset. Every watt that produces fewer tokens is a watt that will never recover those lost revenues, for as long as that infrastructure runs. Jensen's second point is about asset longevity and it is equally important to understand. AI software is evolving every few months from CNNs to Transformers to Mixture of Experts to agentic systems and that pace is not slowing down. A hardware architecture that cannot adapt to new software paradigms has a short useful life, and a short useful life means a high total cost of ownership. Infrastructure built on Nvidia's CUDA ecosystem has a built in software longevity advantage because every new model, framework, and optimization is written for CUDA first. Now apply that framework directly to Nebius, which is the most important stock in the neocloud category. Nebius built its entire infrastructure around full Nvidia integration from the ground up. Nvidia and Nebius announced a formal strategic partnership in March 2026 specifically to develop the next generation of hyperscale AI cloud deployments together. Nebius is already offering Blackwell Ultra GB300 NVL72-powered instances to customers, meaning it has the highest-performance GPU currently available commercially running inside its own infrastructure. The token economics follow directly from the architecture. Contracted power has now passed 3.5 gigawatts, with more than 75% of that capacity owned outright rather than leased. The Meta deal alone is worth $27 billion over five years, and the Microsoft agreement is worth up to $19.4 billion. The 2026 plan targets 480 megawatts of live AI cloud capacity, 150,000 GPUs deployed, and $3.7 billion in annualized revenue implying next twelve month revenue growth of roughly 489%. Q1 2026 revenue was $399 million, up 684% year-over-year, and the CEO said on the earnings call that everything Nebius builds gets sold immediately. Fully booked capacity at an AI cloud running Nvidia's best hardware, inside a power-scarce environment where performance per watt is the direct driver of profitability, means Nebius's revenue ceiling moves in direct proportion to the power it can bring online. CoreWeave, a direct comparable, trades at a materially higher multiple on a smaller contracted power base. Nebius owns more of its capacity outright, has a longer-dated and larger contract backlog on a per-gigawatt basis, and is growing revenue at a faster rate. Milk road remains extremely bullish on Nebius and come join Milk Road Pro and get our full Nebius positioning breakdown and our other AI trades for just a dollar. Link down below!

This company just took news headlines by storm - Jacob Amsterdam just joined its Advisory Board. WHY? Copper recently traded above $14,000 per ton, near historic highs, as demand from AI infrastructure continues accelerating. Just one electric vehicle can use more than 80 kilograms of copper. Some estimates suggest a single large-scale AI data center can require up to 50,000 tons of copper across power systems, transformers, cooling infrastructure, wiring, and grid connections. AI data centers, robots, military systems, renewable energy, and EV production are all increasing global copper demand at the same time. Entire countries are upgrading electrical infrastructure simultaneously. And while demand rises, new copper mines can take more than a decade to develop. That is where NovaRed Mining enters the story. Its Wilmac Copper-Gold Project spans nearly three times the size of Manhattan across prospective copper-gold terrain in British Columbia. As the global race for copper intensifies, companies connected to future copper supply are attracting increasing investor attention. Always do your own research. This is not financial advice.

40个真正有用的GitHub仓库 1. public-apis — 免费API合集 2. build-your-own-x — 边做边学 3. developer-roadmap — 学任何技术 4. free-programming-books — 免费书籍 5. system-design-primer — 掌握系统设计 6. coding-interview-university — 自学计算机 7. the-art-of-command-line — 精通终端 8. project-based-learning — 项目式学习 9. you-dont-know-js — 深入学JavaScript 10. the-book-of-secret-knowledge — 黑客资源 11. tech-interview-handbook — 面试通关 12. awesome-selfhosted — 自建应用 13. javascript-algorithms — 可视化算法 14. 30-seconds-of-code — 实用代码片段 15. gitignore — 各语言模板 16. ollama — 本地运行AI模型 17. langchain — 快速构建AI应用 18. n8n — AI自动化工作流 19. openclaw — 本地AI助手 20. dify — 可视化创建AI代理 21. langflow — 拖拽式AI管道 22. mem0 — AI代理记忆层 23. browser-use — AI控制浏览器 24. ruflo — Claude代理编排 25. crewai — 多代理AI团队 26. hermes-agent — 开源AI代理 27. markitdown — 文件转Markdown 28. maigret — 3000+网站OSINT 29. open-webui — 自建ChatGPT界面 30. aider — 终端AI编程助手 31. agency-agents — 完整AI代理机构 32. tradingagents — 交易多代理框架 33. browserbase-skills — Claude网页SDK 34. autogen — 微软多代理框架 35. metagpt — AI代理软件公司 36. lobe-hub — 可视化多代理平台 37. huggingface-transformers — 现代AI基础 38. cocoindex — 长文本代理引擎 39. freeCodeCamp — 免费编程学习 40. stable-diffusion-webui — 本地AI画图 大多数开发者一个都没保存。聪明人保存了全部40个。

兄弟们，40个有用的GitHub仓库，强烈建议收藏起来！ 1. public-apis — 免费API合集 2. build-your-own-x — 边做边学 3. developer-roadmap — 学任何技术 4. free-programming-books — 免费书籍 5. system-design-primer — 掌握系统设计 6. coding-interview-university — 自学计算机 7. the-art-of-command-line — 精通终端 8. project-based-learning — 项目式学习 9. you-dont-know-js — 深入学JavaScript 10. the-book-of-secret-knowledge — 黑客资源 11. tech-interview-handbook — 面试通关 12. awesome-selfhosted — 自建应用 13. javascript-algorithms — 可视化算法 14. 30-seconds-of-code — 实用代码片段 15. gitignore — 各语言模板 16. ollama — 本地运行AI模型 17. langchain — 快速构建AI应用 18. n8n — AI自动化工作流 19. openclaw — 本地AI助手 20. dify — 可视化创建AI代理 21. langflow — 拖拽式AI管道 22. mem0 — AI代理记忆层 23. browser-use — AI控制浏览器 24. ruflo — Claude代理编排 25. crewai — 多代理AI团队 26. hermes-agent — 开源AI代理 27. markitdown — 文件转Markdown 28. maigret — 3000+网站OSINT 29. open-webui — 自建ChatGPT界面 30. aider — 终端AI编程助手 31. agency-agents — 完整AI代理机构 32. tradingagents — 交易多代理框架 33. browserbase-skills — Claude网页SDK 34. autogen — 微软多代理框架 35. metagpt — AI代理软件公司 36. lobe-hub — 可视化多代理平台 37. huggingface-transformers — 现代AI基础 38. cocoindex — 长文本代理引擎 39. freeCodeCamp — 免费编程学习 40. stable-diffusion-webui — 本地AI画图