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2016年科技閱讀列表

01-29

之前整理的2015年科技閱讀列表［600篇］，有人覺得看不過來，我就把一些個人喜歡的重新列出來，再加到今年列表中，慢慢補充，加上分類標籤，歡迎大家留言翻譯。比如時間在幾周內，站內聯繫。更新 2016/05/23

1. 技術架構

  1. Everyday Algorithms: Elevator Allocation 電梯演算法調度

  2. Le Cloud Blog 系統設計系列 scalability入門

  3. Airbnb Shares The Keys To Its Infrastructure Airbnb基礎架構 翻完
  4. Backend infrastructure at Spotify Spotify架構
  5. Jepsen: On the perils of network partitions 網路分割技術系列
  6. A Comprehensive Guide to Building a Scalable Web App on Amazon Web Services 在AWS上構建大型Web APP指南 進行中
  7. How Instacart Built Its On-Demand Grocery Delivery Service Instacart背後的技術
  8. Pinnability: Machine learning in the home feedPinterest主頁的機器學習

  9. The Rise of the API-based SaaS API作為Saas興起
  10. 5 AWS mistakes you should avoid 5個該避免的AWS錯誤

  11. The Art of the Commit 提交的藝術 進行中

  12. Stack Overflow: The ArchitectureStack Overflow 2016最新架構探秘
  13. Scaling Knowledge at Airbnb Airbnb的知識管理 已翻
  14. How Uber Thinks About Site Reliability Engineering Uber SRE怎麼做

  15. basic infrastructure patterns 基礎架構模式

  16. Designing Schemaless, Uber Engineering Uber無模式數據存儲

  17. Data Architecture in an Anti-Fraud Architecture 反欺詐系統的數據架構 進行中

  18. The Epic Story of Dropbox』s Exodus From the Amazon Cloud Empire長夜讀|Dropbox 出走亞馬遜雲服務帝國的壯麗史詩

  19. How Badoo saved one million dollars switching to PHP7 升級PHP7省了百萬美金

  20. Engineers Shouldnt Write ETL: A Guide to Building a High Functioning Data Science Department 不要寫ETL

  21. Jeff Dean on Large-Scale Deep Learning at Google 已翻

  22. Putting the Squeeze on Trip Data Uber技術

  23. 3 simple reasons why you need to learn Scala 學習Scala的原因

  24. P-values not quite considered harmful P值的作用

  25. 4 reasons why microservices resonate 微模式

  26. Object-oriented vs. functional programming 面向對象還是面向函數

  27. Why a pattern language for microservices? 微模式的設計語言
  28. Working at Netflix 在Netflix工作

  29. Managing Machines at Spotify Spotify如何管理機器

  30. Reclaiming Design Patterns (20 Years Later) ·設計模式20年後

  31. continuous-deployment-at-instagram

  32. Notes on Googles Site Reliability Engineering book

  33. Apache Spark as a Compiler: Joining a Billion Rows per Second on a Laptop
  34. Engineering Intelligence Through Data Visualization at Uber
  35. Distribunomicon

2. 大數據和數據科學系列

  1. Stream processing, Event sourcing, Reactive and making sense of it all 流處理，事件源，響應式

  2. Using logs to build a solid data infrastructure (or: why dual writes are a bad idea) 使用日誌作為可靠數據架構

  3. Bottled Water: Real-time integration of PostgreSQL and Kafka 跟PostgreSQL，Kafka做實時集成

  4. Real-time full-text search with Luwak and Samza Luwak和Samza做實時全文檢索

  5. Turning the database inside-out with Apache Samza Samza調優資料庫

  6. Apache Kafka, Samza, and the Unix Philosophy of Distributed Data Kafka，Samza和Unix的分散式數據設計哲學
  7. The value of Apache Kafka in Big Data ecosystem Kafka在大數據生態系統中的價值 已翻
  8. Distributed Consensus Reloaded: Apache ZooKeeper and Replication in Apache Kafka 分散式重載：kafka中的zookeeper和複製
  9. Putting Apache Kafka To Use: A Practical Guide to Building a Stream Data Platform (Part 1)使用Apache Kafka構建流式數據平台（1）
  10. Putting Apache Kafka To Use: A Practical Guide to Building a Stream Data Platform (Part 2)
  11. Announcing Kafka Connect: Building large-scale low-latency data pipelines Kafka連接：搭建大規模低延遲的數據管道
  12. Introducing Kafka Streams: Stream Processing Made Simple Kafka數據流：讓流處理更輕鬆
  13. Building a high-throughput data science machine 搭建高吞吐的數據科學機器

  14. The Hadoop tipping point hadoop轉折點

  15. Democratizing business analytics 民主化商業分析

  16. Why your next analytics project should be in procurement 分析項目採購

  17. Best practices for data lakes 數據湖的最佳實踐

  18. Embeddable data transformation for real-time streams 實時流的數據處理

  19. Data, technology, and the future of play 數據，技術和未來遊戲

  20. Learning in higher dimensions 在更高維度學習

  21. Hadoop in the cloud 雲端的Hadoop
  22. Approaching big data from a business perspective 從商業角度看大數據

  23. Oil, Gas, and Data 石油，天然氣，數據

  24. Designing great data products 設計偉大的數據產品

  25. Doing Data Science Right Your Most Common Questions Answered 做好數據科學的常見問題

  26. How BuzzFeed Thinks About Data Science Buzzfeed數據科學的思考，進行中
  27. What to look for in a data scientist 數據科學家需要的

  28. Statistics for Software

3. 招聘&面試

  1. What I Learned from Blowing An Interview 從一次失敗的面試學到的東西
  2. The Trick Max Levchin Used to Hire the Best Engineers at PayPal Paypal CTO如何招聘最好工程師，完成
  3. How to Hire a Rock Star Engineer 如何招聘頂級工程師
  4. My favorite interview question 我最喜歡的面試題
  5. What-are-the-questions-that-can-be-asked-when-the-interviewer-asks-Any-questions 還有什麼問題要問
  6. On Interviewing Software Engineers 怎麼面試工程師
  7. Ace the coding interview, every time 攻克代碼面試

  8. How Stack Overflow Does Technical Interviews Stack Overflow怎麼做技術面試
  9. This Is How You Identify A-Players (In About 10 Minutes) During An Interview 在面試10分鐘內找到最好的人
  10. Startup Interviewing is Fucked 創業公司面試

  11. How to Hire 怎麼招聘

  12. 3 Tips for Onboarding New Hires Using Quip 新人報道指南

  13. Firing People 如何開除

  14. Layoffs 怎麼知道會被解僱

  15. Effective Code Reviews 高效代碼審查

  16. Improving Our Engineering Interview Process 改進工程面試流程

  17. i-quit-hiring-is-broken

  18. This Startup Has a Radical Way to Encourage Work-Life Balance For Its People

  19. Hiring is Broken... And It Isnt Worth Fixing

  20. three-years-in-san-francisco

4. 管理&成長

  1. How to get rich in tech, guaranteed. 怎麼通過技術變富 已翻
  2. Fail at Scale 快速變化中的可靠性
  3. When I Learned That Computers Have Soul 當計算機有靈魂 已翻

  4. #define CTO 定義CTO
  5. Do the Right Thing做正確的事 已翻

  6. The Surprising Secret to Being a Good Boss 成為好老闆的秘訣

  7. The Highest-Leverage Activities Arent Always Deep Work 影響力大的工作不一定有多深

  8. The Secret to Growing Your Engineering Career If You Dont Want to Manage 不走管理路線你還能職業發展的秘密
  9. Calculating the Value of Time: How Much is Your Time Really Worth?過去的時間管理都弱爆了！看矽谷人如何為自己的一小時定價
  10. The software engineer』s guide to asserting dominance in the workplace 工程師一周應該怎麼過
  11. Sleep deprivation is not a badge of honor 不要熬夜 進行中

  12. What is Craftsmanship and Why is it Important? 技術精益重要性

  13. Being data-driven: It『s all about the culture 數據驅動

  14. Five principles for applying data science for social good

  15. Beyond the Venn diagram 超越Venn類型

  16. What I learned about software architecture from running a marathon 從馬拉松想到軟體架構

  17. Defining a reactive microservice 定義微服務

  18. Educating data 教育數據

  19. Make Money Need Practice 賺錢需要經驗

  20. Its Okay Not To Lead 不當老大也沒事

  21. Autobiography of Blind Programmer 盲人程序員自傳

  22. Those entry level startup jobs they are now mostly dead ends 初級創業公司工作死路一條

  23. Everything is possible but nothing is free 一切皆有可能，但沒有免費午餐

  24. Salary in my Startup: a Thought Experiment 創業公司薪水揭秘

  25. Coding Like a Girl 女孩怎麼編程

  26. Art and Math and Science, Oh My!藝術，數學和科學 已翻

  27. The Munger Operating System: A Life That Really Works

5. 創業分享

  1. my-y-combinator-experience我的Y COMBINATOR 之旅
  2. How to Design a Better Pitch Deck如何設計出更好的融資PPT？
  3. How to build a good onboarding process for new hires at a startup創業公司如何培訓新員工
  4. How I validated my startup ideazhuanlan.zhihu.com/p/20
  5. After the Layoffs 裁員之後
  6. 156 Startup Failure Post-Mortems 156家創業失敗啟示，1/3完成

  7. 10 tips for moving from programmer to entrepreneur 從程序員進化到企業家 進行中

  8. When to join a startup 什麼時候加入創業公司 譯完

  9. Letter To A Young Programmer Considering A Startup 給想創業的年輕程序員的信

  10. How to Time TravelAirbnb CEO告訴你如何寫一篇優秀的品牌（軟）文
  11. Ten classic books that define tech 十本書推薦
  12. How do you validate your startup idea before quitting your current job 在你辭職前如何驗證創業想法

  13. 6 questions every founder should ask before they raise capital 融資前要問的6個問題

  14. The Best Time to Invest Startup 投資創業公司最佳時候

  15. Ideas for Small Business 一個人的公司

  16. Instagram Investment Instagram早期投資人

  17. The New Rules of Startup Fundraising 創業融資的新規則

  18. From side project to 250 million daily requests 從兼職項目到2.5億次日訪問

  19. Up or Out: Solving the IT Turnover Crisis

  20. elevate-yourself-with-side-projects

6. 行業公司和人物採訪

  1. Head of Amazon Web Services on Finding the Next Great Opportunity AWS主管尋找下一個偉大計劃
  2. 10-lessons-from-10-years-of-awsAWS 運營 10 周年學到的 10 條經驗教訓

  3. Mark Zuckerberg tackles question on what he would do as Twitter CEO如果扎克伯格是Twitter的CEO，他會怎麼做？
  4. How Zenefits Crashed Back Down To Earth謊言、酒宴：融資5.8億美元的矽谷獨角獸，瘋狂失控中
  5. Why I left the best job in the world The Startup 為何我離開世界上最好的工作
  6. A Decade at Google 在Google工作十年的感悟
  7. Hadoop creator Doug Cutting on evolving and succeeding in open source Doug 談Hadoop進化和開源
  8. Google and Facebook Team Up to Open Source the Gear Behind Their Empires Google 和FB談數據中心的較量 進行中

  9. Facebook Doesn』t Make as Much Money as It Could Facebook錢還沒賺夠
  10. What My PhD Was Like 讀博是怎麼過的

  11. What Technology Will Look Like In Five Years 5年後技術什麼樣 翻譯完
  12. Etsy CTO Q&A: We Need Software Engineers, Not Developers 我們要的是工程師，不是開發者
  13. Curation and Algorithms 人工挑選和演算法

  14. 10X Durability 10倍可靠
  15. We』re in a brave, new post open source world 在開源世界中生存

  16. From fleeing Vietnam in a refugee boat to becoming Uber』s CTO從難民到Uber首席技術官：一個倖存者的故事

  17. What Will You Do After White-Collar Work? 白領工作後能做啥？
  18. Lyft To Uber: The Race Is On Lyft和Uber的戰爭
  19. How Jeff Bezos Became a Power Beyond Amazon突破Amazon，Jeff Bezos非凡影響力的崛起之路

  20. Searching For Google CEO Sundar Pichai, The Most Powerful Tech Giant Youve Never Heard Of Google CEO你沒聽說過的超強巨人
  21. Why This Tech Bubble is Worse Than the Tech Bubble of 2000 現在科技泡沫比2000年還大？

  22. The sharing economy: A big step toward making Marshall McLuhans Global Village a reality 共享經濟
  23. Algorithms of the Mind 思想的演算法

  24. The inside story of how Amazon created Echo, the next billion-dollar business no one saw coming亞馬遜 Echo 誕生記：起初無人看好，如今它卻擁有十億美元的商機

  25. Three Lessons On Innovation I Learned During My 12 Years At Apple在蘋果工作12年，職場老兵告訴你如何創新
  26. Linux at 25: Q&A With Linus TorvaldsLinux 25 歲了，我採訪了大神 Linus

  27. Founder of Pandora on Lessons from Near Dot Com Bust to Billion Dollar IPO Pandora從破產到十億俱樂部
  28. WeWork』s Radical Plan to Remake Real Estate With Code WeWork顛覆房地產

  29. MY YEAR IN STARTUP HELL 50歲在創業公司

  30. The Story Behind Siri 聽「Siri之父」講述Siri背後的故事
  31. Building Internet Startup Chinese Style 互聯網創業要像中國學習

  32. bloomberg.com/features/

  33. Inside Palantir, Silicon Valleys Most Secretive Company

  34. Inside OpenAI, Elon Musk』s Wild Plan to Set Artificial Intelligence Free

  35. interivew-with-shantanu-sinha

  36. Inside Evan Spiegels very private Snapchat Story
  37. Andrew Ng: Why 『Deep Learning』 Is a Mandate for Humans, Not Just Machines
  38. I was losing $1 million a day, every day for 18 months: Meet Chris Anderson, the man behind TED talks

7. 人工智慧&機器學習

  1. How AI Is Feeding China s Internet Dragon AI是怎麼適應中國互聯網巨龍的（百度）

  2. Silicon Valley Looks to Artificial Intelligence for the Next Big Thing 矽谷把AI作為下一個大事
  3. Artificial Intelligence Finally Entered Our Everyday World AI最後進入我們每天的生活
  4. The Future of Chat Is not AI 聊天的未來不是AI
  5. AlphaGo and the Limits of Machine Intuition AlphaGo和機器覺醒

  6. The current state of machine intelligence 2.0重磅機器智能 2.0 生態圖譜
  7. The future of machine intelligenceO』Reilly 報告：機器智能的未來
  8. Learning from Tay Tay學到的
  9. Learning from AlphaGo AlphaGo學習到的

  10. Risto Miikkulainen on evolutionary computation and making robots think for themselves 如何讓機器人自我思考

  11. How to build and run your first deep learning network 怎麼搭建第一個深度學習網路

  12. Predictive modeling: Striking a balance between accuracy and interpretability

  13. How human-machine collaboration has automated the data catalog 人工和機器如何合作生成數據目錄

  14. Building a business that combines human experts and data science 把專家和數據科學結合

  15. Unsupervised learning, attention, and other mysteries 非監督學習，注意和其他神秘

  16. AI『s dueling definitions AI的定義

  17. In search of a model for modeling intelligence 模型智能的搜索

  18. What is deep learning, and why should you care? 深度學習是啥

  19. Compressed representations in the age of big data 大數據時代的壓縮表示

  20. Machine learning in the wild 機器學習野蠻生長

  21. Training and serving NLP models using Spark MLlib 通過spark庫做自然語言處理

  22. Wouldn『t it be fun to build your own Google? 能自己建個Google嗎

  23. Small brains, big data 小大腦，大數據

  24. On the evolution of machine learning 機器學習的進化

  25. Evolutionary computation: Stepping stones and unexpected solutions 進化計算

  26. Data has a shape 數據有型

  27. Geoffrey Hinton, the godfather of deep learning, on AlphaGo前沿 | 專訪Geoffrey Hinton：人工智慧會繼續發展，請不要誤用

  28. Million-dollar babies 矽谷為了搶人，做AI的學生有福了

  29. Uber CTO reveals how Travis Kalanick hired him and offers advice for entrepreneurs Uber CTO揭秘招聘和對企業家建議

  30. My path to OpenAI

8. 產品設計&用戶增長

  1. How Slack Uses Slack Slack是如何使用Slack的
  2. The Design Sprint 設計的周期
  3. On building product at Medium Medium如何做產品的
  4. Duolinguo reach 110M Users 多鄰國怎麼把用戶發展到上億的 ，已翻

  5. Simple Design is What You Need, Not What You Want 簡單設計你需要的，而不是想要的

  6. Growth is a system, not a bag of tricks 增長是一個系統
  7. Design, Process, and Collaboration at Stripe Stripe設計，流程和合作
  8. Product Hunt Rise Product Hunt 花了3年多成長故事

  9. The Rise, Fall, and Rise of Bitly: How a Free Link Shortener Became a Real Business Adventures in Consumer Technology 短鏈服務如何掙錢的

  10. How Apple Built 3D Touch 蘋果手機的3D觸摸怎麼做的
  11. Hacking Word-of-Mouth: Making Referrals Work for Airbnb[Growth Hacking] Airbnb 邀請系統的實現過程

  12. How Pinterest increased MAUs with one simple trick Pinterest實現MAU增長的小技巧

  13. The vision, mission, and strategy for Coinbase Coinbase的使命和戰略

  14. Mobile UX Design: What Makes a Good Notification? 手機UX設計：怎麼做好通知

  15. Joel Marsh on the science of design 設計科學

  16. UX for beginners: Key ideas UX入門

  17. Prototyping for physical and digital products 物理數字產品的原型設計

  18. Snapchats Ladder Snapchat的梯子

  19. Freemium Conversion Rate: Why Spotify Destroys Dropbox by 667% Spotify的轉化率

  20. The Story of AdMob: How One MBA Dropout Sold His Business to Google for $750 million AdMob 賣給Google 7.5億

  21. Why Facebook And Mark Zuckerberg Went All In On Live Video Facebook為何全力做視頻直播

  22. Y Combinator and The One Metric that Matters 集中在一個指標上
  23. Instagram and Facebook are Dead Instagram和Facebook都死了

  24. Instagram is stupid Instagram太傻了

  25. The Scientific Marketing Strategy Behind Exponential Growth

9. 前沿技術（虛擬現實，實時計算）

  1. Timoni West on nailing the virtual reality user experience 虛擬現實體驗

  2. The evolution of open source is a good thing 開源進化是好事

  3. A new infrastructure for biology 生物的新架構

  4. The IoT is a natural ecosystem for streaming analytics IOT是流分析的自然生態

  5. Stream processing and messaging systems for the IoT age IOT的實時消息處理

  6. Embeddable data transformation for real-time streams 實時流計算
  7. The big data market從Hadoop洞悉大數據市場：路漫漫其修遠兮

  8. What『 next for big data applications? 下一個大數據應用是什麼
  9. Distributed systems performance solutions require real-time intelligence 分散式系統需要實時智能

10. 其他

  1. Chinese Scions』 Song: My Daddy』s Rich and My Lamborghini』s Good-Looking 老爸很有錢，蘭博基尼很酷

  2. The long march from China to the Ivies 中國學生進入哈佛的長征之路
  3. Priscilla Chan, in rare interview, tells how her goals with Mark Zuckerberg are shaped by personal story她讓扎克伯格死心塌地，原因就兩個字
  4. Heavy Recruitment of Chinese Students Sows Discord on U.S. Campuses 美國大學招了太多中國學生
  5. The American Scholar: Saving the Self in the Age of the Selfie 不要再自拍了
  6. 「I had so many advantages, and I barely made it」: Pinterest engineer on Silicon Valley

  7. How To Manage Developers When Youre A Non-Tech Founder

  8. How to be the most productive person in your office a€」 and still get home by 5:30 p.m.

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關注如下我的微信公眾號「董老師在矽谷」，關注矽谷趨勢，一起學習成長。


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