与「Airbnb」相关的搜索结果

这个开源项目系统整理了 35 家 AI 公司的 AI 工程师面试题，全部来自 “公开报告的面试经历” 来自 @outcome_school 团队 @pallavishekhar_ 开源发布，内容几乎涵盖了 OpenAI、Anthropic、DeepMind、xAI、DeepSeek、Kimi、GLM 等 AI Labs，Cursor、Cognition、ElevenLabs 等 AI Native 团队和 Nvidia、Microsoft、Amazon、Apple 等头部大厂。 覆盖的岗位头衔也非常多：AI Engineer、LLM Engineer、Gen AI Engineer、ML Engineer、Research Engineer、Applied Scientist、FDE、MLOps/LLMOps 工程师等。 开源地址： # 内容架构：一个精心设计的双层结构 第一层：跨公司通用题（Common Questions）。 作者把在多家公司反复出现的题目只列一次，标注"Asked at"哪些公司，按十大主题组织： 1. LLM 内部机制与架构 — attention 缩放因子、KV cache 内存公式推导、MQA/GQA/MLA、FlashAttention、BPE、RoPE/YaRN、Chinchilla scaling laws、MoE、解码采样策略、lost-in-the-middle、RMSNorm、SwiGLU 2. 推理、服务与 GPU 性能 — prefill vs decode、continuous batching、PagedAttention、投机解码、量化（FP16→FP4）、五种并行策略、TTFT/TPOT 指标、H100 上的 roofline 计算、vLLM/SGLang/TensorRT-LLM 选型、“如何把服务成本降 10 倍” 3. RAG 与检索 — 分块策略、BM25 vs 稠密检索、重排序器、HyDE、权限感知检索、ANN 索引、索引新鲜度、答案归因 4. Agent 与工具调用 — ReAct、MCP、工具 schema 设计、多智能体编排、Agent 记忆、循环终止条件、人类审批 5. 微调与对齐 — RLHF/DPO/GRPO/RLVR 全谱系、LoRA/QLoRA 数学、灾难性遗忘、“提示 vs RAG vs 微调”决策框架、蒸馏、reward hacking 6. 评估与可观测性 — LLM-as-judge 及其偏差、幻觉检测、基准污染、Agent 评估、回归门禁 7. 安全与负责任 AI — 提示注入（直接/间接）、OWASP LLM Top 10、护栏、Constitutional AI、红队 8. 多模态与语音 — VLM、语音 Agent 延迟预算、barge-in 打断处理、级联 vs 端到端语音、ASR/TTS 评估 9. AI 系统设计 — 十类高频设计题（千万级文档企业 RAG、代码助手、客服 Agent、Text-to-SQL、LLM 网关、数亿用户聊天服务等） 10. 编码题 — 从零实现 attention、KV cache、BPE、采样；LRU 缓存、令牌桶限流器、异步批处理器、SSE 流解析器、最小 Agent 循环 第二层：35 家公司的专属章节，分为五大梯队： 1. 前沿实验室（12 家）：Anthropic、OpenAI、Google DeepMind、Meta、xAI、Mistral、Cohere、DeepSeek、月之暗面（Kimi）、智谱（GLM）、阿里（Qwen）、Sarvam AI（印度） 2. 大厂 AI 组织：Microsoft、Amazon、Apple、NVIDIA、Tesla，以及一组消费级 ML 公司（Uber/Netflix/LinkedIn/Airbnb/Pinterest/Spotify） 3. AI 基础设施公司：Databricks、Groq、Together AI、Hugging Face、Scale AI、Perplexity 4. AI 原生产品公司：Cursor、Cognition（Devin）、Sierra、Harvey（法律）、Glean、 AI（机器人）、Waymo 5. 前向部署/企业 AI：Palantir # 题目分布透露的行业信号同样值得关注 1. 公司的差异化考察方向，和它的商业模式严丝合缝。 这是最能体现整理功力的地方： · DeepSeek、月之暗面、智谱、Qwen 的题目深度绑定自家论文——MLA、auxiliary-loss-free 负载均衡、Multi-Token Prediction、MuonClip、DualPipe、长上下文扩展、GLM 的 thinking 模式。面试这些公司等于面试它们的论文，还要求 PyTorch 从零实现 MoE 路由。 · Groq 的题全是 SRAM-only 架构下的 roofline 重推演：“没有 HBM，decode 的 roofline 论证哪里变了”、“确定性在 p99 层面到底买到什么”。 · Apple 清一色端侧：3B 模型在手机上跑、PTQ vs QAT、不采集用户内容的前提下用设备信号改进模型、30+ 语言区无法记录用户内容的评估方案。 · CharacterAI 是推理经济学：“我们的服务成本被 KV cache 而非权重主导，降一个数量级，代价是什么”。 · Harvey（法律）和 Abridge（医疗） 考的是领域约束下的工程：200 页信贷协议里第 140 页的条款依赖第 8 页的定义术语怎么检索、生成的病历中出现了患者没提过的药怎么当作安全事故处理、PHI 如何约束整个架构。 · Palantir 的招牌是 "decomposition" 轮：把“一家货运铁路公司每年因机车非计划停机损失数千万”分解成工程计划。 2. 编码轮的形态正在发生实质性变化。 文档里反复出现的一类题，与传统 LeetCode 明显不同： · “实现一个内存 KV 存储：先 SET/GET/DELETE，再加事务 BEGIN/COMMIT/ROLLBACK，包括嵌套事务”（OpenAI、xAI 都问） · “给你一个 LLM 推理引擎的调度器类，其中一个方法是空壳，没有规格没有文档。说说你头三十分钟干什么”（xAI） “重构这 120 行能跑但很乱的代码，不许破坏测试”（OpenAI） · “对 5 万个文档跑 LLM 调用，API 限 100 并发、偶发 429 和超时，把 Python 写出来”（Anthropic） 考察重心从算法记忆转向增量需求下的代码演进能力、并发正确性、真实工程约束下的取舍。 3. “AI 协作轮”作为新题型已经进入正式面试流程。 这是文档里最前沿的信号： · Anthropic 部分机器学习岗有 AI-collaboration 轮：现场给你 Claude，考察的是你如何指挥它和验证它的产出，而不是你自己写。 · Meta 2026 年的流程新增三阶段 AI 辅助编码轮（探索修复 → 实现新功能 → 扩展改进）。 · Cursor 的 onsite 是两天在真实 Cursor 代码库上做一个功能（或 8 小时远程版），明确考核你对 AI 工具的使用效率和自主 scoping 能力。 · Sierra 给你两小时和任意 AI 工具，看你选择做什么。 xAI 有四小时限时产品构建。 4. FDE 成为一级岗位类别。 Anthropic、OpenAI、Databricks、Scale、Together、Sierra、Harvey、ElevenLabs、Palantir 的章节里都有 "Applied and Forward-Deployed Scenarios" 专属题库——典型题目如“企业客户说 Claude 幻觉太多，你是驻场工程师，头 48 小时做什么”。这对应了 AI 公司向企业交付方式的转变：模型能力差距收窄后，落地能力成为差异化。 5. 硬核系统题的普及。 "H100 上 70B 模型 batch size 1 的 roofline 计算"、"估算 70B 模型的 GPU 显存（权重 + KV cache + 激活 + 碎片）"、"p99 延迟在部署后翻倍但模型没变，走一遍诊断”——这类题横跨 NVIDIA、Together、OpenAI、Perplexity 等多家，说明推理性能的量化直觉已成为 AI 工程师的通用素养，而非基础设施工程师的专属。

Should you chase hype or ignore it? The tech industry has been debating this for decades. So at @Sequoia, we dug into 20 years of hype data. This summer, I worked with Sequoia intern @ochonaut to measure hype over the past 2 decades. The chart below ranks the most hyped topics on Hacker News for every year since 2007. Under each year sits the most valuable company founded that year. Here are a few observations: 1/ The top company founded in a given year is rarely related to the hype of that period. Airbnb was founded in 2008, when the top topic was Google. Uber arrived in 2009, while the conversation revolved around low-level programming. Anthropic came in 2021, while the internet was consumed by crypto. Chasing hype rarely leads to enduring outcomes. The top companies of recent years have yet to be decided. 2/ New trends announce themselves five to six years early. LLMs first cracked the top 15 in 2016 and took until 2022 to hit #1#. AI coding entered at #12# in 2021 and tops the list in 2026. Crypto entered in 2011 before 2017 and 2021 peaks. “New” trends don’t appear out of nowhere, and internet subcommunities are often the first to know where the puck is headed. 3/ Long-term “hype” is a durable signal. The “Musk-Verse” has been a top 15 topic for every one of the past 14 years. Sustained attention on the internet is rare and tends to mark something real. Next up, we want to run the same analysis with sources like X and LinkedIn. If that's of interest, give us some encouragement and we'll share the results.

Hot take… isn’t it kinda crazy that nobody is really using AI Agents? I don’t mean software engineers or AI early adopters. I mean “college friends talking about it in group chat,” the feeling you got when everyone started using Instagram or TikTok. These frontier AI models are *insane* (as are the harnesses & tool calls & the like). And every large tech co has an AI agents platform, not to mention all the YC startups doing vertical agents. Yet all of your friends and family outside of tech — who spend all day staring at their iPhones and get paid to work in browser tabs — don’t really care or find themselves using any AI agents yet. Yes ChatGPT, Claude, etc. are extremely popular… but if you look at the engagement data the vast majority of people are still using these aI chat tools like a glorified Google + Grammarly. That’s why the AGI labs are all pushing desktop apps for Codex, Cowork, etc. so hard to non-technical ppl. And yes exceptions for lawyers and customer service but even those have some asterisks and exceptions to rule. Look I’m not saying the ChatGPT moment for AI Agents is not coming… it most definitely is! Remember we pivoted from Arc to Dia precisely because we believe computing is going to be radically reimagined around these AI primitives. No doubt. But that’s my point: it’s just so surprising it hasn’t happened yet because all of the tech you’d need is there. Again if you stop for a second and think about it… for all the press and money and hype and models and crazy ARR numbers… this “AI Agent” moment does not *feel* like the other breakthrough tech moments we’ve lived through (e.g. think the shift to Stories via Snapchat & Instagram, or shift to on-demand via Uber/Airbnb/Doordash). Which is a long way of saying: if you can figure out the answer to “why” most people don’t care about AI agents yet (and have no enduring interest in using them) — especially since the models and harnesses are here and ready — the answer to that question will allow you to capture a lot of marketshare and make a lot of money in 2027. Theoretically, the tech is ready for AI Agents to totally transform how we work and live our lives… but alas the general public dgaf… that’s the generational puzzle to solve for the next 12 months for anyone not working on the models themselves.

Mısır’da geceliği 100 pound olan ve ilanında sadece içi gösterilen Airbnb evini kiralayan turist, dışarıyı görüntülemeye başlayınca dışardakiler peş peşe “No, no, no!” diyerek kamerayı kapatmasını istedi; çevrenin özellikle gizlenmeye çalışılması dikkat çekti.

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First Principles Ep. 4 with Alvin Roth Long before crypto made coordination programmable, Nobel Prize winner Alvin Roth was designing markets where coordination could save lives, matching doctors to hospitals, students to schools, and kidney donors to the patients who need them. Roth explains why markets are not just natural forces, but engineered systems; why the details of timing, congestion, incentives, and trust can make or break a marketplace; and why some of the most important markets are the ones where simply exchanging money can’t do the work. Hosted by @Tim_Roughgarden with @skominers. 00:00 Intro: Why market design matters 04:18 The economist as engineer 08:09 When theory meets the real world 07:02 Fixing the medical residency match 15:32 Why markets unravel 18:22 Redesigning NYC high school admissions 28:05 The hidden problem of congestion 34:47 How kidney exchange saves lives 45:26 How the internet changed market design 48:25 Airbnb, Uber and smarter marketplaces 51:28 Repugnant transactions and moral economics 53:32 When markets need social support 54:32 The unexpected effects of criminalizing surrogacy 01:04:58 Preference signals and the job market 01:18:53 A broken market: resettling refugees and other migrants

Solana创始人当年刚创立Solana时孩子刚出生，全靠老婆在Airbnb上班养家。 他自己疯狂开了1000场投融资会，结果没人愿意给钱！ 他说创业根本不是运筹帷幄，而是到处起火，你只能选择看着哪里的火先烧，然后忍受几年的痛苦。 现在Solana市值飙到470亿美元。

很多人喜欢盯着TVL来算市值，现在以太坊的市值已经低于TVL，于是不少人认为以太坊被低估了。但是Visa每年处理15万亿美金的交易，市值只有6000亿，纽交所每年结算60万亿美金的股票，市值只有800亿，Airbnb托管了3万亿美金的房产，市值只有900亿，但你从来不会因此去买它们的股票🤷‍♂️