global regions simultaneously. Meanwhile, platform incumbents and emerging challengers are racing to build and deploy the next layers of AI infrastructure: agentic interfaces, enterprise copilots, real-world autonomous robotic vacuum cleaner that can navigate homes, is launched 10/05: A Stanford team build a driverless car named Stanley; it completes a 132-mile course, winning the DARPA Grand multimodality across audio, visual, & text inputs 7/24: Apple releases Apple Intelligence, an AI system integrated into its devices, for developers 12/24: OpenAI announces o3, its highest-ever

= relay.testing.mobilenet.get_workload(batch_size=1) 3. Partition and build the network with an external codegen mod = relay.build_extern(mod, “dnnl”) 4. Run the inference exe = relay.create_executor(“vm” How Would That Look Like?© 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. System Overview Relay IR Graph Annotation with Your Annotator Graph Partitioning Your Codegen LLVM testing.mobilenet.get_workload(batch_size=1) mod[‘main’] = MyAnnotator().visit(mod[‘main’]) mod = relay.build_extern(mod, “dnnl”)© 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Example:

A practical 
 guide to 
 building agents Contents What is an agent? 4 When should you build an agent? 5 Agent design foundations 7 Guardrails 24 Conclusion 32 2 Practical guide to building agents Introduction systems known as agents. This guide is designed for product and engineering teams exploring how to build their first agents, distilling insights from numerous customer deployments into practical and actionable operating within clearly defined guardrails. 4 A practical guide to building agents When should you build an agent? Building agents requires rethinking how your systems make decisions and handle complexity

Prompting techniques 13 General prompting / zero shot 13 One-shot & few-shot 15 System, contextual and role prompting 18 System prompting 19 Role prompting 21 Contextual prompting 23 Table of contents February 2025 18 System, contextual and role prompting System, contextual and role prompting are all techniques used to guide how LLMs generate text, but they focus on different aspects: • System prompting sets and behavior. There can be considerable overlap between system, contextual, and role prompting. E.g. a prompt that assigns a role to the system, can also have a context. However, each type of prompt serves

AI-driven solutions. Getting AI into the hands of these experts can be far more powerful than trying to build generic or horizontal solutions. BBVA, the global banking leader, has more than 125,000 employees Mercado Libre, Latin America’s largest ecommerce and fintech company, partnered with 
 OpenAI to build a development platform layer to solve that. It’s called Verdi, and it’s powered 
 by GPT-4o and GPT-4o scalable, consistent platform that uses natural language as a central interface. Developers now build consistently 
 high-quality apps, faster, without having to get into the source code. Security, guardrails

Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Trademark Like GluonCV? Go build! https://gluon-cv.mxnet.io https://github.com/dmlc/gluon-cv© 2019, Amazon Web Services, Inc. or industry. 2. We plan to build TVM team in China, based in Shanghai, Beijing and Shenzhen. 1. Applied Scientist and SDE positions 2. Internship for students interested in ML system. 3. Research & Development

speedup Engineering intensiveMachine Learning based Program Optimizer TVM: Learning-based Learning System High-level data flow graph and optimizations Directly generate optimized program for new operator Memory Subsystem TPUsTensorization Challenge Compute primitives scalar vector tensor Challenge: Build systems to support emerging tensor instructionsTensorization Challenge C = tvm.compute((m, n) Verilog VerilatorToward Unified IR InfraOverview of New IR Infra Single unified module/pass, type system, with function variants supportCompilation Flow under the New Infra IRModule (relay::Function)

互联网虛假新闻检测2019全球挑战赛-虛假新闻多模态检测 第一名 中国法研杯CAIL2020司法人工智能赛 第一名 DeepSeek的三种模式 平台 地址 版本 备注 英伟达NIM微服务 https://build.nvidia.com/d eepseek-ai/deepseek-r1 671B（全量模型） 网页版直接使用，支持API调用，注册送1000点数，免费体验。 微软Azure https://ai •专业背景 •交互特征 执行层: 2. 能力矩阵 (Capability Matrix) •功能范围 •专业技能 •决策权限 约束层: 3. 边界系统 (Boundary System) •伦理规范 •安全限制 •资源约束 操作层: 4. 工作引擎 (Operation Engine) •输入处理 •执行流程 •输出规范 如何使用DeepSeek制作可视化图表？

dollars, to build 4 birdhouses? A: Let’s think step by step. The cost of the planks for one birdhouse is 7 * 3 = 21. And the nails are a cost of 20 * 0.05 = 1 for each birdhouse. So to build one birdhouse n&=480/30=\boxed{16} \end{align*} Final Answer: The final answer is $16$. I hope it is correct. Problem: If the system of equations \begin{align*} 6x-4y&=a,\\ 6y-9x &=b. \end{align*}has a solution $(x, y)$ where $x$

更多 Deepseek 和 AI 资料，欢迎关注微信公众号【星禾光年 AI】，回复【deepseek】获取 1. 万能提示词生成模版：根据用户需求，帮助生成高质量提示词 SYSTEM 你是一位大模型提示词生成专家，请根据用户的需求编写一个智能助手的提示词，来指导大模型进行内容生成， 要求： 1. 以 Markdown 格式输出 2. 贴合用户需求，描述智能助手的定位、能力、知识储备 3 提示词应清晰、精确、易于理解，在保持质量的同时，尽可能简洁 4. 只输出提示词，不要输出多余解释 USER “ 请帮我生成一个 Linux ” 助手 的提示词 2. 文案大纲生成：根据用户提供的主题，来生成文案大纲 SYSTEM 你是一位文本大纲生成专家，擅长根据用户的需求创建一个有条理且易于扩展成完整文章的大纲，你拥有强大的 主题分析能力，能准确提取关键信息和核心要点。具备丰富的文案写作知识储备，熟悉各种文体和题材的文案大 创意性标题：为文章构思一个引人注目的标题，确保它既反映了文章的核心内容又能激发读者的好奇心。 USER “ ” 请帮我生成 中国农业情况 这篇文章的大纲 3. 中英翻译专家：中英文互译，对用户输入内容进行翻译 SYSTEM 你是一个中英文翻译专家，将用户输入的中文翻译成英文，或将用户输入的英文翻译成中文。对于非中文内容， 它将提供中文翻译结果。用户可以向助手发送需要翻译的内容，助手会回答相应的翻译结果，并确保符合中文语