Reading

From balance sheets to psychology, my investing reading follows one line: learn to count first, then learn to wait. Graham and Lynch are the starting point; Housel and Sperandeo cover the harder part, which is managing yourself.

1. 
   金钱心理学 （全新增订版）[美]摩根·豪泽尔 ★★★★★

   Investing is a psychology problem before it is a maths problem. My whole review: fourteen pieces of advice for the road to financial independence.

   My notes →
2. 
   专业投机原理（典藏版）维克托·斯波朗迪 ★★★★★

   Called a book on speculation, it reads as a book on decision discipline and self-knowledge, with a lot of psychology, economics and politics folded in. Not a chart manual.

   My notes →
3. 
   投资中最简单的事邱国鹭 ★★★★★

   Qiu Guolu turns value investing into common sense: cheap is what matters, and good companies deserve patience for a good price. Easy to know, hard to do.

   My notes →
4. 
   战胜华尔街Beating the Street（修订版）彼得·林奇 约翰·罗瑟查尔德 ★★★★★

   Lynch's second book, each chapter a post-mortem on a real position. Nobody explains finding winners in everyday life more concretely.

5. 
   上市公司财报分析实战邹丹编著 ★★★★★

   A hands-on guide to reading annual reports. Start with the primer, then practise on real filings with this one.

6. 
   聪明的投资者（第4版，注疏点评版）[美]本杰明·格雷厄姆 [美]贾森·兹威格　著

   The source of value investing. Chapter 8 on Mr. Market and chapter 20 on the margin of safety are worth rereading every few years.

After years in e-commerce, social apps and ride sharing, and a few products of my own, this list is how I came to understand why companies work the way they do: how to think from zero to one, why users stay, how organisations grow.

1. 
   从0到1：开启商业与未来的秘密彼得·蒂尔 布莱克·马斯特斯 ★★★★★

   Competition is for losers; real value comes from monopoly. It makes you ask whether anything you are building has a secret.

2. 
   腾讯是怎么长大的吴晓波 ★★★★★

   A twenty-year chronicle of the Chinese internet, from OICQ to WeChat. Wu Xiaobo writes it like a novel, but every decision is sourced.

3. 
   零售的哲学铃木敏文 ★★★★★

   The 7-Eleven founder's method. My review in one line: the only constant is that what customers want keeps changing.

   My notes →
4. 
   干法稻盛和夫 ★★★★★

   Inamori Kazuo on why we work. Thin and quiet, and it lands every time I reread it.

5. 
   上瘾：让用户养成使用习惯的四大产品逻辑尼尔·埃亚尔 瑞安·胡佛 ★★★★★

   The four-step model of habit-forming products: trigger, action, reward, investment. Unavoidable if you build products.

6. 
   重新定义公司 谷歌是如何运营的（奇点系列）埃里克·施密特 乔纳森·罗森伯格 ★★★★★

   How Google manages smart creatives. Hiring, meetings, decisions: every rule is directly usable.

7. 
   一分钟经理人（新版）肯·布兰佳 斯宾塞·约翰逊 ★★★★★

   A one-hour introduction to management: one-minute goals, praise and reprimands. It reads differently once you have led a team.

Most of these I read during my Android and backend years. The ones I picked share one trait: they explain one thing all the way down, how a computer computes, how memory is reclaimed, how an operating system schedules.

1. 
   编码：隐匿在计算机软硬件背后的语言查尔斯.佩措尔德

   From telegraphs and relays all the way up to a CPU. The best introduction to computers I have read, and it assumes nothing.

2. 
   算法（第4版）Robert Sedgewick Kevin Wayne

   Sedgewick's textbook explains why each data structure is designed the way it is. It builds intuition in a way problem sets do not.

3. 
   垃圾回收的算法与实现中村成洋 相川光

   Garbage collection explained thoroughly. After it, the JVM and Go collectors both make sense.

4. 
   操作系统导论雷姆兹·H.阿帕希杜塞尔 安德莉亚·C.阿帕希杜塞尔

   OSTEP: free, clear and a little funny. Virtualisation, concurrency and persistence, taught better than any course I took.

5. 
   深入理解Java虚拟机：JVM高级特性与最佳实践（第2版）周志明

   I only read it properly after seven years of Java and Android. Reading it earlier would have saved me many memory and GC mistakes.

My machine learning started with writing a neural network by hand, and I picked up reinforcement learning later for the Mahjong AI. This list leans hands-on, with as few formulas as possible.

1. 
   Python神经网络编程塔里克·拉希德

   A neural network from scratch in two hundred pages of plain Python. The fastest way I know to understand backpropagation.

2. 
   Python深度学习弗朗索瓦·肖莱

   The Keras author's introduction to deep learning, the most approachable one, with examples that actually run.

3. 
   机器学习实战：基于Scikit-Learn、Keras和TensorFlow（原书第2版）[法]奥雷利安·杰龙

   The hands-on textbook, from Scikit-Learn to Keras. I stopped at 96% because I had not needed the last chapter yet.

4. 
   深度强化学习实践（原书第2版）马克西姆·拉潘

   My reference while building the Mahjong AI, with runnable implementations from DQN to PPO.

Books read for no particular purpose, which turn out to be the highest rated. History, biology, fiction, and the one that made me decide to build my own things, Hackers & Painters.

1. 
   黑客与画家（10万册纪念版）保罗·格雷厄姆 ★★★★★

   Paul Graham on why programmers are like painters and why you should make your own things. This is where my side projects started.

2. 
   人类简史：从动物到上帝尤瓦尔·赫拉利 ★★★★★

   Cognitive, agricultural and scientific revolutions, seventy thousand years told as one story. It changes how you read the news.

3. 
   自私的基因（20世纪经典著作）理查德·道金斯 ★★★★★

   The gene is the unit of selection and we are its vehicles. Once that lands, a lot of behaviour makes sense.

4. 
   中央帝国的财政密码郭建龙 ★★★★★

   Two thousand years of Chinese history seen through the treasury: behind every reform, war and dynasty is a question of money.

5. 
   明朝那些事儿（全集）当年明月 ★★★★★

   Three hundred years of Ming history told like a storyteller, read straight through. If serious history does not stick, start here.

6. 
   白夜行东野圭吾 ★★★★★

   Finished it in one night and could not put it down. I had not felt that in a long time.

   My notes →
7. 
   动物庄园（英文版）乔治·奥威尔 ★★★★★

   Read in the original, barely a hundred pages, on how power goes bad one step at a time. Worth rereading every few years.

   My notes →